2693 Machine Learning jobs in Bangalore

1. Job Title: Machine Learning (ML) Architect

Summary:The Machine Learning Architect designs and oversees AI/ML architecture for scalable enterprise solutions. This role sets technical direction, drives ML operations (MLOps), ensures high model performance, security, compliance, and bridges data, engineering, and business objectives.

Key Responsibilities:

• Architect and maintain ML pipelines spanning data preparation, model training, deployment, monitoring, and retraining.
• Define standards/best practices for MLOps, feature management, model versioning, CI/CD, and model registry.
• Collaborate with DevOps, Security, Compliance and Data Governance teams on infrastructure and policies.
• Evaluate, select, and integrate appropriate frameworks and platforms (PyTorch, TensorFlow, AWS Sagemaker, Vertex AI, Kubeflow, etc.).
• Optimize computational resource utilization and cost for cloud/hybrid environments.
• Lead technical design reviews, root cause analyses, and system troubleshooting.
• Document architecture and mentor teams in scalable ML solution design.

Required Skills:

• Deep knowledge of ML algorithms, statistical modeling, data engineering, and distributed systems.
• Strong programming (Python, Scala, Java) and hands-on MLOps exposure.
• Cloud expertise (AWS, Azure, GCP), especially ML-native services.
• Proficiency in containerization/orchestration (Docker, Kubernetes).
• Familiarity with CI/CD pipelines, GitOps, and Infrastructure as Code.
• Excellent communication, system documentation, and leadership skills.

Preferred Qualifications:

• Experience with Explainable AI, responsible AI, and ML governance.
• Prior experience leading large-scale deployments of ML solutions.

Tools & Technologies:Python, PyTorch, TensorFlow, MLFlow, Airflow, AWS, GCP, AzureML, Docker, Kubernetes, Apache Spark.

Experience:8+ years in ML/Data Science/Software Engineering, 3+ years architecture leadership.

Education:Bachelor's/Master's in Computer Science, Data Science, or similar technical field.

Job Type: Full-time

Pay: ₹4,000, ₹6,000,000.00 per year

Work Location: In person

What you will enjoy doing

• You will develop, train, test and deploy machine learning models in various fields such as

computer vision, LLMs, tabular and time series data.

• Experimenting with novel deep learning based technologies such as self-supervised learning

and generative AI will also be part of your tasks

• You will support in setting up the MLOps infrastructure for existing and new AI products

• Additionally you will work directly with customer data and set up data pipelines to collect,

curate, transform and version data

You'll be working in the Artificial Intelligence team, AI global corporate division engaged with real business challenges and opportunities in multiple countries. Focus of this role is to support the AI team with extending existing and building new AI products for a vast amount of uses cases across

business and value chain. You'll collaborate across different business and corporate functions in international team composed of Project Managers, Data Scientists, Data and Software Engineers

in the AI team and others in the Global AI team.

As a Machine Learning Enginee, you will be working closely with our global AI team to create and

implement sustainable AI models, algorithms, and software solutions for our global industrial gases and engineering services operations. You will participate in the collection, analysis, interpretation, and output of large amounts of data using advanced AI techniques like deep learning, NLP, and computer vision.

What Makes You Great

• You have a Bachelor or Masters Degree in Data Science, Computational

Statistics/Mathematics, Computer Science or related field

• You know the basics of neural networks and have an initial practical experience with deep

learning frameworks like PyTorch and Tensorflow.

• You have experience in backend and API development with Python (FastAPI, Flask) or similar

• You demonstrate knowledge of and basic practical experience with Object Oriented

Programming, design patterns, algorithms and data structures, version control systems (e.g.

Git)

• Having basic experience with cloud platforms (e.g. Azure), Docker and Kubernetes is a plus

• Fluent English

What you will enjoy doing

• You will develop, train, test and deploy machine learning models in various fields such as

computer vision, LLMs, tabular and time series data.

• Experimenting with novel deep learning based technologies such as self-supervised learning

and generative AI will also be part of your tasks

• You will support in setting up the MLOps infrastructure for existing and new AI products

• Additionally you will work directly with customer data and set up data pipelines to collect,

curate, transform and version data

You'll be working in the Artificial Intelligence team, Linde's AI global corporate division engaged with

real business challenges and opportunities in multiple countries. Focus of this role is to support the

AI team with extending existing and building new AI products for a vast amount of uses cases across

Linde’s business and value chain. You'll collaborate across different business and corporate functions

in international team composed of Project Managers, Data Scientists, Data and Software Engineers

in the AI team and others in the Linde's Global AI team.

As a Machine Learning Enginee, you will be working closely with our global AI team to create and

implement sustainable AI models, algorithms, and software solutions for our global industrial gases

and engineering services operations. You will participate in the collection, analysis, interpretation, and

output of large amounts of data using advanced AI techniques like deep learning, NLP, and computer

vision.

What Makes You Great

• You have a Bachelor or Masters Degree in Data Science, Computational

Statistics/Mathematics, Computer Science or related field

• You know the basics of neural networks and have an initial practical experience with deep

learning frameworks like PyTorch and Tensorflow.

• You have experience in backend and API development with Python (FastAPI, Flask) or similar

• You demonstrate knowledge of and basic practical experience with Object Oriented

Programming, design patterns, algorithms and data structures, version control systems (e.g.

Git)

• Having basic experience with cloud platforms (e.g. Azure), Docker and Kubernetes is a plus

• Fluent English

Why You Will Love Working With Us

At Linde, you will have the opportunity to think outside the box, work on innovative topics, and deliver

impactful solutions to our internal customers. You’ll be part of a team that values inclusivity, fosters

creativity, and welcomes diverse perspectives, making Linde an employer of choice.

We act responsibly towards our shareholders, business partners, employees, society, and the

environment. Linde is committed to technologies and products that combine customer value with

sustainable development.

What We Offer You

In addition to an attractive compensation package, we offer flexible working hours and various

benefits to support your professional and personal development. Our culture values work-life

balance, helping you to combine career ambitions with family commitments.

Have We Inspired You?

We look forward to receiving your application.

Machine Learning Engineer – Model Training & Optimization

About Unlox

Unlox is an AI-driven learning and career platform, trusted by universities and recognized by NASSCOM. We’re building scalable AI systems to power personalized learning and career readiness for the next generation.

We are hiring a Machine Learning Engineer (Training & Optimization) who will focus on curating datasets, training, fine-tuning, and optimizing models to deliver high-performing AI systems.

Responsibilities

Curate and preprocess domain-specific datasets.

Train and fine-tune transformer-based models with LoRA, QLoRA, quantization, distillation.

Run experiments and track results to improve efficiency and accuracy.

Build training pipelines and evaluation systems for continuous improvement.

Collaborate with AI researchers and backend teams to integrate outputs into applications.

Requirements

Wrok Experience: 1 - 4 years

Proficiency in Python, PyTorch, and ML frameworks (Hugging Face, DeepSpeed, Accelerate).

Experience in fine-tuning transformer models.

Familiarity with tokenizer training, dataset pipelines, and evaluation benchmarks.

Knowledge of parameter-efficient tuning techniques.

Bonus

RLHF exposure or contributions to open-source AI projects.

Salary- As per market standards with performance based bonuses

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About the Company:

UptimeAI is leading the way in predictive analytics and AI-driven solutions to optimize operational uptime and reduce downtime for industrial and enterprise clients. Our innovative platform harnesses cutting-edge data science to deliver actionable insights, ensuring maximum efficiency and reliability. UptimeAI uniquely combines Artificial Intelligence with Subject Matter Knowledge from 200+ years of cumulative experience to explain interrelations across upstream/downstream equipment, adapt to changes, identify problems, and give prescriptive diagnosis like a human expert would.

About the Role:

We're looking for a highly skilled and hands-on ML End-to-End Engineer. You'll need deep practical experience across the entire machine learning lifecycle, from data ingestion and model development to robust backend integration and scalable production deployment. We want someone who not only understands the theory but can demonstrate significant real-world application and problem-solving at every stage of ML product development.

Responsibilities:

• ML Model Development & Optimization:
• Algorithm Proficiency: Proven experience designing, training, and optimizing diverse ML models, including strong expertise in supervised learning, and significant practical experience with unsupervised learning (e.g., clustering, dimensionality reduction, anomaly detection) and reinforcement learning algorithms (e.g., Q-learning, policy gradients). You should be able to discuss specific challenges encountered during model development across these paradigms and how you resolved them.
• Frameworks: Hands-on expertise with PyTorch, TensorFlow, and scikit-learn.
• Libraries: Strong proficiency with NumPy, Pandas, SciPy, Seaborn, and Plotly for data manipulation, analysis, and visualization.
• Feature Engineering: Demonstrable experience in effective feature engineering, selection, and transformation techniques.
• Data Engineering & Management for ML:
• Data Pipelining: Proven experience building and managing robust data pipelines for ML, including data ingestion, cleaning, transformation, and validation.
• Database Proficiency: Strong command of SQL and NoSQL databases (e.g., PostgreSQL, MongoDB) for storing and retrieving data relevant to ML models.
• Real-time Data Streams: Expertise with Apache Kafka for building and managing real-time data ingestion and processing pipelines.
• Backend Development for ML Applications:
• API Development: Demonstrable experience designing, building, and maintaining RESTful APIs for serving ML model predictions.
• Programming Language & Frameworks: Strong proficiency in Python with practical experience using either Flask or FastAPI for backend service development.
• System Design: Ability to design scalable, fault-tolerant, and high-performance backend systems to support ML inference.
• MLOps & Production Deployment:
• Containerization: Expertise in containerization technologies (e.g., Docker) for packaging ML models and their dependencies.
• Orchestration: Experience with container orchestration tools (e.g., Kubernetes) for deploying and managing ML services at scale.
• CI/CD for ML: Proven ability to set up and manage CI/CD pipelines specifically for ML model training, testing, and deployment (e.g., Jenkins, GitLab CI, GitHub Actions).
• Monitoring & Logging: Experience in implementing robust monitoring, alerting, and logging solutions for production ML systems to ensure performance, reliability, and data drift detection
• Model Performance & Reliability:
• Performance Tuning: Proven ability to identify and resolve performance bottlenecks in ML models and backend services. This includes experience with fine-tuning models and applying techniques to extract maximum performance from them, such as quantization, pruning, or model compression.
• Model Versioning & Experiment Tracking: Experience with tools and practices for model versioning, experiment tracking (e.g., MLflow, DVC), and reproducibility.
• General Engineering & Problem Solving:
• Competitive Coding / Algorithmic Problem Solving: Demonstrated proficiency in competitive coding platforms (e.g., LeetCode, HackerRank, TopCoder, Codeforces) or a strong, demonstrable foundation in algorithms and data structures, showcasing exceptional problem-solving abilities.
• Security Best Practices for ML Systems: Understanding and implementation of security best practices for ML models, data, and APIs.

Qualifications:

• 5+ years of experience as ML Engineer in high-growth SaaS or product startups
• Strong problem-solving and engineering mindset, with a keen eye for scalability, reliability, and efficiency.
• Excellent communication skills for conveying complex technical information to both technical and non-technical stakeholders.
• Adaptable and enthusiastic about working in a fast-paced, product-driven environment.
• Proactive in learning new ML technologies, backend frameworks, and deployment methodologies
• Comfortable working in a fast-paced, ambiguous startup environment

Why to join UptimeAI:

• Impact Industry-Wide Change: Contribute to transformative solutions that significantly improve operational efficiency and reliability for global clients.
• Collaborative and Growth-Oriented Environment: Join a talented, passionate team that values innovation, continuous learning, and professional growth.
• Opportunities for Leadership and Innovation: Lead pioneering projects, influence product development, and shape the future of industrial AI solutions.

Pay range and compensation package:

(Pay range or salary or compensation)

Equal Opportunity Statement:

(Include a statement on commitment to diversity and inclusivity.)

Glance - An InMobi Group Company

Founded in 2019, Glance is a consumer technology company that operates disruptive digital platforms including Glance, Roposo, and Nostra. Glance’s ’smart lock screen’ inspires consumers to make the most of every moment by surfacing relevant experiences without the need for searching and downloading apps. Glance Lock Screen is currently available on over 450 million smartphones worldwide. Roposo is a LIVE platform that is revolutionizing live experiences, through a unique, immersive, creator-led approach. Nostra is the largest gaming platform in India and Southeast Asia, offering gamers engaging ways to discover, play, watch, learn and compete.

Headquartered in Singapore, Glance is an unconsolidated subsidiary of InMobi Group and is funded by Jio Platforms, Google, and Mithril Capital. For more information visit glance.com, nostra.gg, and roposo.com

What should you know about joining Glance?

At Glance, we walk the talk – free yourself, dream big, and chase your passion! On joining, you’ll have opportunities to make an immediate impact on mission-critical projects, as you work with highly capable and ambitious peer groups.

Be rewarded for your autonomy even as you collaborate. Ideate, innovate, and inspire by leveraging bleeding-edge tech to disrupt consumer experiences.

While you work, we’ll take care of nourishing your body, mind, and soul. This includes daily meals, gym, trainings, tech tools, and regular unwind sessions. Also, feel free to bring your kids – even the furry ones – to the office!

What You Will Be Doing

As a Machine Learning Engineer , you will help design, develop, and deploy scalable ML systems and services that power consumer experiences across Glance platforms. You will work closely with Data Scientists and cross-functional teams to drive ML solutions from ideation to production.

Key Responsibilities:

• Architect and implement end-to-end ML pipelines for experimentation, model development, and deployment.
• Build microservices and APIs to serve ML models and AI-driven features.
• Design and develop proof-of-concepts and scalable AI/ML-based products.
• Collaborate with product, engineering, and content (newsroom) teams to define, test, and launch new ML features.
• Explore and integrate new tools, platforms, algorithms, and ML frameworks.
• Own project delivery with a focus on performance, reliability, and scalability.
• Document solutions and communicate effectively with stakeholders.

What We’re Looking For

• 5+ years of hands-on experience as an ML Engineer, with a track record of delivering real-world ML solutions.
• Strong background in Computer Science, Mathematics, or Statistics (Bachelor’s, Master’s, or PhD).
• Proven ownership of end-to-end ML projects, from prototype to production.
• Solid experience working with Recommender Systems and personalization.
• Proficiency in Python , Golang , Spark , and related open-source ML/data frameworks.
• Strong understanding of statistics, algorithms , and applied math.
• Ability to work independently and collaboratively in cross-functional teams.
• Excellent technical documentation and communication skills.
• Experience in a research-driven or data science-intensive environment is a plus.

TE-4 Years and above

Location- Bangalore/Chennai/Hyderabad

NP- 15-30 Days max

JOB DESCRIPTION

Join our fast‑growing team to build a unified platform for data analytics, machine learning, and generative AI. You’ll integrate the AI/ML toolkit, real‑time streaming into a backed feature store, and dashboards—turning raw events into reliable features, insights, and user‑facing analytics at scale.

What you’ll do

• Design and build streaming data pipelines (exactly‑once or effectively‑once) from event sources into low‑latency feature serving and NRT and OLAP queries.
• Develop an AI/ML toolkit: reusable libraries, SDKs, and CLIs for data ingestion, feature engineering, model training, evaluation, and deployment.
• Stand up and optimize a production feature store (schemas, SCD handling, point‑in‑time correctness, TTL/compaction, backfills).
• Expose features and analytics via well‑designed APIs/Services; integrate with model serving and retrieval for ML/GenAI use cases.
• Build and operationalize Superset dashboards for monitoring data quality, pipeline health, feature drift, model performance, and business KPIs.
• Implement governance and reliability: data contracts, schema evolution, lineage, observability, alerting, and cost controls.
• Partner with UI/UX, data science, and backend teams to ship end‑to‑end workflows from data capture to real‑time inference and decisioning.
• Drive performance: benchmark and tune distributed DB (partitions, indexes, compression, merge settings), streaming frameworks, and query patterns.
• Automate with CI/CD, infrastructure‑as‑code, and reproducible environments for quick, safe releases.

Tech you may use

Languages: Python, Java/Scala, SQL

Streaming/Compute: Kafka (or Pulsar), Spark, Flink, Beam

Storage/OLAP: ClickHouse (primary), object storage (S3/GCS), Parquet/Iceberg/Delta

Orchestration/Workflow: Airflow, dbt (for transformations), Makefiles/Poetry/pipenv

ML/MLOps: MLflow/Weights & Biases, KServe/Seldon, Feast/custom feature store patterns, vector stores (optional)

Dashboards/BI: Superset (plugins, theming), Grafana for ops

Platform: Kubernetes, Docker, Terraform, GitHub Actions/GitLab CI, Prometheus/OpenTelemetry

Cloud: AWS/GCP/Azure

What we’re looking for

1. 4+ years building production data/ML or streaming systems with high TPS and large data volumes.
2. Strong coding skills in Python and one of Java/Scala; solid SQL and data modeling.
3. Hands‑on experience with Kafka (or similar), Spark/Flink, and OLAP stores—ideally ClickHouse.
4. GenAI pipelines: retrieval‑augmented generation (RAG), embeddings, prompt/tooling workflows, model evaluation at scale.
5. Proven experience designing feature pipelines with point‑in‑time correctness and backfills; understanding of online/offline consistency.
6. Experience instrumenting Superset dashboards tied to ClickHouse for operational and product analytics.
7. Fluency with CI/CD, containerization, Kubernetes, and infrastructure‑as‑code.
8. Solid grasp of distributed systems and architecture fundamentals: partitioning, consistency, idempotency, retries, batching vs. streaming, and cost/perf trade‑offs.
9. Excellent collaboration skills; ability to work cross‑functionally with DS/ML, product, and UI/UX.
10. Ability to pass a CodeSignal prescreen coding test.

Grid Dynamics (Nasdaq:GDYN) is a digital-native technology services provider that accelerates growth and bolsters competitive advantage for Fortune 1000 companies. Grid Dynamics provides digital transformation consulting and implementation services in omnichannel customer experience, big data analytics, search, artificial intelligence, cloud migration, and application modernization. Grid Dynamics achieves high speed-to-market, quality, and efficiency by using technology accelerators, an agile delivery culture, and its pool of global engineering talent. Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the US, UK, Netherlands, Mexico, India, Central and Eastern Europe.

To learn more about Grid Dynamics, please visit . Follow us on Facebook , Twitter , and LinkedIn .

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Hiring: Machine Learning Engineers (ML 2 / ML 3) – Bangalore

Are you passionate about building customer-facing AI solutions that make a real impact?

Do you thrive in fast-paced, product-driven environments where ownership and accountability are valued?

We are looking for ML Engineers (Level 2/3) to join our growing AI/ML team in Bangalore. This is an exciting opportunity to work on Generative AI, personalization, and intelligent search systems that will directly shape the experience of millions of users.

Key Responsibilities

As a part of our ML team, you will:

• Design and implement scalable ML solutions that directly align with product and business goals.
• Develop, test, and optimize ML models for production environments with high availability and low latency.
• Build Generative AI features such as conversational interfaces, intelligent assistants, and personalized experiences.
• Partner with product managers, data scientists, and engineers to integrate ML into customer-facing applications .
• Monitor and evaluate model performance post-deployment; iterate to improve accuracy, latency, and UX.
• Drive best practices in experimentation, reproducibility, and deployment workflows .
• Participate actively in code reviews, design discussions, and technical deep dives .
• Work closely with stakeholders to translate business needs into ML-driven solutions .

Job Description 1 – Machine Learning Engineer

Role: Machine Learning Engineer

Experience: 5+ years

Location: Pune, Bangalore, Hyderabad, Mohali, Chennai, Bhubneshwar

Role Summary

We are seeking a Machine Learning Engineer to design, develop, and deploy advanced AI/ML models that drive innovation and business value. The ideal candidate will have hands-on experience in building scalable ML pipelines, applying generative AI frameworks, and optimizing models for performance and efficiency.

Key Responsibilities

• Design, develop, and test AI/ML models adhering to architecture and coding standards.
• Perform data preprocessing, feature engineering, and model evaluation to ensure accuracy and reliability.
• Write technical specifications, documentation, and reusable code .
• Conduct code reviews and provide constructive feedback to peers.
• Collaborate with cross-functional teams to integrate ML models into production applications.
• Optimize models for performance, scalability, and low latency .
• Stay updated with the latest advancements in AI/ML, deep learning, and generative AI frameworks .

Required Skills

• Strong knowledge of Machine Learning algorithms and techniques.
• Proficiency in Python for ML/AI development.
• Experience with Generative AI frameworks (LLMs, Transformers, etc.) .
• Expertise in data analysis, feature engineering, and model evaluation techniques .
• Familiarity with code review practices and collaborative development tools .
• Knowledge of cloud platforms (AWS, Azure, or GCP) is a plus.

Job Description 2 – ML/Backend Engineer

Role: ML/Backend Engineer

Experience: 5+ years

Location: Pune, Bangalore, Hyderabad, Mohali, Chennai, Bhubneshwar

Role Summary

We are looking for an ML/Backend Engineer who can combine expertise in machine learning with backend system development. The role involves designing AI/ML models while ensuring seamless backend integration, API development, and efficient data handling for production-ready deployments.

Key Responsibilities

• Design, develop, and test AI/ML models and solutions aligned with system architecture.
• Implement backend logic, APIs, and data pipelines for smooth ML model deployment.
• Work on database design and management (SQL/NoSQL) to support large-scale ML applications.
• Conduct code reviews , ensuring performance and maintainability.
• Collaborate with other developers, data scientists, and product teams to integrate solutions.
• Ensure backend systems are scalable, secure, and cloud-ready .
• Stay up-to-date with emerging ML, backend, and cloud technologies .

Required Skills

• Proficiency in Python for both ML and backend development.
• Hands-on experience with Machine Learning algorithms and Generative AI frameworks .
• Strong knowledge of FastAPI (or similar Python frameworks) for API design.
• Expertise in database management (SQL/NoSQL) .
• Experience with cloud platforms (AWS, Azure, or GCP) for deployment.
• Strong understanding of data analysis, feature engineering, and model evaluation .