18,195 Data Science Applications jobs in India

Job Description:

We’re seeking a hands-on ML Engineer to transform cutting-edge research

into production features. You will own Tiny LLM on-device inference,

predictive analytics, dynamic escalation workflows, gamified modules, and

localisation pipelines—working end-to-end from model training to mobile

deployment.

Required Qualifications:

• Bachelor’s or Master’s in Computer Science, Engineering, or related field

• 2+ years’ hands-on experience with transformer architectures, fine-tuning,

and model inference

• Strong proficiency in Python, including libraries/frameworks such as

PyTorch, TensorFlow, sciki t- learn, and fastAPI

• Proven track record deploying ML models to production (TorchServe, ONNX,

HuggingFace Inference API)

• Solid data engineering skills: data lakes, batch pipelines, structured logging

(e.g., Airflow, Spark)

• Familiarity with edge/embedded ML: quantisation (4–8 bits), memory

footprints (20–50 MB), RAM budgets (100–300 MB)

• Experience configuring API Gateways, server-less functions, and message

queues (Kafka, Celery)

• Deep understanding of data security, privacy regulations (GDPR/HIPAA-

inspired), consent flows, and audit logging

• Expertise in localisation and NLP: neural translation, dialect adaptation,

multi-modal (text & voice) processing

• Comfortable in Agile/CI-CD environments with containerised micro-services

(Docker, Kubernetes)

Key Responsibilities:

• Develop and optimise Tiny LLM inference pipelines.

• Implement dynamic risk-based escalation workflows (sentiment 0.0–1.0;

thresholds 0.3–0.7; horizon 3–14 days)

• Build gamification engines (points: 10–100; streaks: 3–30 days; quest

windows: 1–7 days) to boost retention

• Integrate neural machine translation with regional dialect support (latency:

100–300 ms; BLEU: 30–50) for text and voice interfaces

• Architect offline data synchronisation (intervals: 1–24 hrs; payload: 5–50 kB)

and ensure seamless async sync under <50 kB/s bandwidth

• Deploy models and services using TorchServe, ONNX, or HuggingFace

Inference API, and manage server-less scaling, API Gateway, Kafka/Celery

queues

• Collaborate with backend and mobile teams to meet performance targets (UI

load: 100–300 ms; battery drain: 1–3 %/hr)

• Embed security and compliance: AES-256 & TLS 1.3 encryption, consent

management, legal disclaimers, audit-grade logging

• Maintain high availability (99.9 % SLA), automated retraining cycles (1–4

weeks), and structured logging for analytics

• Write clean, production-grade Python code to support data pipelines, model

training, inference, and integration.

Preferred Skils:

• Prior work in digital health or mental-wellness applications

• Familiarity with mobile frameworks (React Native, Flutter)

• Experience designing or measuring gamification metrics

• Knowledge of federated learning or privacy-preserving ML

What We Offer:

- Competitive contract rate

- Remote work arrangement

- Opportunity to work on exciting projects with a talented team

If you’re passionate about building humane AI that transcends infrastructure

barriers and delivers personalised, proactive mental care, we’d love to hear

from you. Please share your resume and a brief note on a production ML system you’ve delivered end-to-end.

At eBay, we're more than a global ecommerce leader — we’re changing the way the world shops and sells. Our platform empowers millions of buyers and sellers in more than 190 markets around the world. We’re committed to pushing boundaries and leaving our mark as we reinvent the future of ecommerce for enthusiasts.

Our customers are our compass, authenticity thrives, bold ideas are welcome, and everyone can bring their unique selves to work — every day. We're in this together, sustaining the future of our customers, our company, and our planet.

Join a team of passionate thinkers, innovators, and dreamers — and help us connect people and build communities to create economic opportunity for all.

Machine Learning Engineer

Date: Jul 3, 2025

Job Description

Looking for a company that inspires passion, courage and creativity, where you can be on the team shaping the future of global commerce? Want to shape how millions of people buy, sell, connect, and share around the world? If you’re interested in joining a purpose driven community that is dedicated to crafting an ambitious and inclusive work environment, join eBay – a company you can be proud to be with.

Our Recommendations team works on delivering recommendations at scale and in near real time to our buyers on our website and native app platforms. Recommendations are a core part of how our buyers navigate eBay’s vast and varied inventory. Our team develops state-of-the-art recommendations systems, including deep learning based retrieval systems for personalized recommendations, machine learned ranking models, GenAI/LLM powered recommendations, as well as advanced MLOps in a high volume traffic industrial e-commerce setting.

We are building cutting edge recommender systems powered by the latest ML, NLP, LLM/GenAI/RAG and AI technologies. Additionally, we are building production integrations with Google GCP Vertex AI platforms to supercharge our item recommendation algorithms. Come join our innovative engineering and applied research team!

This Is An Opportunity To

• Influence how people will interact with eBay’s recommender systems in the future, and how recommender systems technology will evolve
• Work with unique and large data sets of unstructured multimodal data representing eBay's vast and varied inventory, including billions of items and millions of users
• Develop and deploy state-of-the-art AI models to production which have direct measurable impact on eBay buyers
• Deploy big data technology and large scale data pipelines
• Drive marketplace GMB as well as advertising revenue via organic and sponsored recommendations

Qualifications

• MS in Computer Science or related area with 1+ years of relevant work experience (or BS/BA with 3+ years) in Engineering / Machine Learning / AI
• Experience building large scale distributed applications and expertise in an OO/functional language (Scala, Java, etc.)
• Experience building with no sql databases and key value stores (MongoDB, Redis, etc)
• Generalist with a can do attitude and willingness to learn/pick up new skill sets as needed
• Experience with using cloud services is a plus (GCP is a double plus)
• Experience with big data pipelines (Hadoop, Spark, Flink) is a plus
• Experience in AI applied research and industrial recommendation systems is a plus
• Experience with Large Language Models (LLMs) and prompt engineering is a plus

Exp:3+ years

Budget: 18 lpa

Key Responsibilities:

• Design and implement ML models for personalization, recommendations, NLP, and agentic AI systems.
• Build, test, and deploy ML models to production.
• Create and optimize feature engineering pipelines.
• Collaborate with cross-functional teams to integrate ML solutions.
• Monitor and improve model performance.

Required Skills:

• Strong Python skills (NumPy, Pandas, Scikit-learn).
• Deep learning with PyTorch or TensorFlow.
• Experience with recommendation libraries (LightFM, Surprise).
• Knowledge of LangChain/AutoGPT for agentic AI.
• NLP expertise with Hugging Face Transformers.
• Experience deploying ML models to production.

Qualifications:

• Advanced degree in Computer Science, ML, or related field.
• 3+ years of relevant ML/AI experience.

Hiring: MLOps Engineer | Bangalore (Hybrid – 4 Days WFO)

Job Title: MLOps Engineer

Location: Bangalore (Hybrid – 4 days work from office)

Job Type: Full-Time

Experience: 3+ years

About the Role

We are looking for a passionate MLOps Engineer who can bridge the gap between Data Science and Production systems. You’ll work with Data Scientists, DevOps, and Software Engineers to build scalable ML infrastructure, automate workflows, and ensure smooth deployments.

Key Responsibilities

• Design, develop & maintain CI/CD pipelines for ML models
• Collaborate with Data Scientists to deploy scalable ML solutions
• Automate validation, retraining & monitoring pipelines
• Ensure reliability, security & scalability of ML infrastructure
• Manage containerized ML apps using Docker & Kubernetes
• Monitor model performance & data drift in production

Required Skills

• 3+ years in MLOps / DevOps / ML Engineering
• Strong in Python, Bash & scripting
• Experience with ML platforms (MLflow / Kubeflow / SageMaker)
• Cloud expertise (AWS / Azure / GCP )
• CI/CD tools (Jenkins, GitHub Actions, etc.)
• Mandatory: Experience in Psypark

Preferred Skills

• IaC tools (Terraform)
• Data engineering / ETL pipelines
• Monitoring tools (Prometheus, Grafana)

Why Join?

• Work at the intersection of AI, DevOps & Scalability
• Contribute to production-grade ML systems
• Growth-focused role with cutting-edge tech exposure

Machine Learning Engineer

About Astreya:

Astreya offers comprehensive IT support and managed services. These services include Data

Center and Network Management, Digital Workplace Services (like Service Desk, Audio Visual, and

IT Asset Management), as well as Next-Gen Digital Engineering services encompassing Software

Engineering, Data Engineering, and cybersecurity solutions. Astreya's expertise lies in creating

seamless interactions between people and technology to help organizations achieve operational

excellence and growth.

Responsibilities:

• Design, develop, and deploy machine learning models and algorithms to solve complex business problems and drive data-driven decision making
• Collaborate with cross-functional teams including data engineers, software engineers, and business stakeholders to understand requirements and translate business objectives into scalable ML solutions
• Build and maintain robust ML pipelines for data preprocessing, feature engineering, model training, validation, and deployment
• Optimize model performance through hyperparameter tuning, feature selection, and algorithmic improvements
• Implement MLOps best practices including model versioning, monitoring, and automated retraining workflows
• Ensure data quality, integrity, and security throughout the ML lifecycle
• Scale ML systems to handle large datasets and high-throughput inference requirements
• Conduct A/B testing and experimentation to validate model performance and business impact
• Monitor deployed models for drift, performance degradation, and bias detection
• Create comprehensive documentation including technical specifications, model cards, and deployment guides for stakeholders

Professional & Technical Skills:

• Must have strong proficiency in Python and experience with core ML libraries such as scikit-learn, XGBoost, LightGBM, and CatBoost
• Must have hands-on experience with deep learning frameworks including TensorFlow, PyTorch
• Must have solid understanding of machine learning algorithms, statistical methods, and model evaluation techniques
• Must have experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies Docker or Kubernetes
• Need to have proficiency in SQL and experience working with both structured and unstructured datasets
• Need to have knowledge of big data technologies such as Spark, Hadoop, or distributed computing frameworks
• Must be familiar with MLOps tools and platforms such as MLflow, Kubeflow, or similar model lifecycle management systems
• Must have experience with version control systems (Git) and CI/CD pipelines for ML workflows
• Need to have knowledge of data visualization tools and libraries such as Matplotlib, Seaborn, Plotly, or Tableau
• Must be familiar with software engineering best practices, including code testing, debugging, and performance optimization
• Need to have understanding of software development methodologies such as Agile or Scrum
• Possess excellent analytical and problem-solving skills with ability to work independently and in team environments

Additional Information:

• Must have Bachelor's or Master's degree in Computer Science, Machine Learning, Statistics, Mathematics, or related quantitative field
• Preferred 3+ years of experience in machine learning engineering or related roles
• Preferred experience with real-time inference systems and model serving architectures

Preferred knowledge of specialized domains such as computer vision, natural language processing, or recommendation systems

Role Overview

We are seeking a hands-on AI/ML Engineer with proven expertise in designing, building, and deploying intelligent chatbots. The ideal candidate will not only be technically strong but also capable of leading a small team of junior engineers, setting technical direction, and ensuring high-quality delivery.

Key Responsibilities

• Architect, design, and implement AI-driven chatbot solutions using state-of-the-art frameworks (e.g., Rasa, Dialogflow, LangChain, LLM APIs).

• Apply Natural Language Processing (NLP) and Machine Learning techniques for intent classification, entity recognition, and conversation management.

• Lead end-to-end chatbot development lifecycle – from data preparation and model training to deployment and monitoring.

• Guide junior engineers through code reviews, mentorship, and technical leadership.

• Collaborate with product, UX, and business teams to ensure chatbot solutions meet user and business requirements.

• Continuously evaluate and integrate new tools, frameworks, and AI advancements (e.g., LLM fine-tuning, vector databases, prompt engineering).

• Ensure production-grade performance, scalability, and security of deployed solutions.

Required Skills & Experience

• 6–10 years of professional experience in AI/ML and software engineering, with at least 3+ years building chatbot solutions.

• Proficiency in Python and ML libraries (TensorFlow, PyTorch, Scikit-learn, spaCy, Hugging Face, etc.).

• Strong expertise in NLP techniques, conversational AI frameworks (Rasa, Dialogflow, Botpress, LangChain, etc.), and LLM APIs (OpenAI, Anthropic, etc.).

• Experience with cloud deployment (AWS/Azure/GCP) and containerization (Docker/Kubernetes).

• Knowledge of vector databases (Pinecone, Weaviate, FAISS, Milvus) for retrieval-augmented generation (RAG).

• Hands-on experience integrating chatbots with enterprise systems, APIs, and messaging platforms (Teams, Slack, WhatsApp, Web, etc.).

• Strong understanding of ML lifecycle management (MLOps), model deployment, monitoring, and retraining.

• Demonstrated ability to lead small technical teams and deliver projects in Agile environments.

Nice-to-Have

• Experience in Generative AI (LLMs, fine-tuning, prompt engineering).

• Background in analytics, recommendation systems, or voice-enabled bots.

• Open-source contributions or published work in chatbot/AI.