1378 Machine Learning Engineer jobs in New Delhi

AI/ML Engineer (Mtech or PhD) – LLMs, RAG, Reinforcement Learning

Position
: AI/ML Engineer (Mid-Level)

Experience
: Not required.

Location
: Noida, UP

Employment Type
: Full-time

Studies
: Mtech or PhD

About the Role:

We are building enterprise-grade
AI/ML solutions
, including SLMs,
LLMs, RAG-based knowledge systems
, reinforcement learning, and agentic AI

As a Mid-level AI/ML Engineer, you will design, train, and deploy machine learning models, collaborate with our product and engineering teams, and ensure scalable integration of AI models into real-world applications.

This role is ideal for someone with a strong hands-on background in
NLP, deep learning, and reinforcement learning, who is eager to grow by working on cutting-edge AI projects at scale
.

Key Responsibilities

• Design, train, and fine-tune ML/DL models (with focus on transformers, SLMs, LLMs, and recommender systems).

• Implement RAG pipelines using vector databases (Pinecone, Weaviate, FAISS) and frameworks like LangChain or LlamaIndex.

• Contribute to LLM fine-tuning using LoRA, QLoRA, and PEFT techniques.
• Work on reinforcement learning (RL/RLHF) for optimizing LLM responses.

• Build data preprocessing pipelines for structured and unstructured datasets.

• Collaborate with backend engineers to expose models as APIs using FastAPI/Flask.

• Ensure scalable deployment using Docker, Kubernetes, AWS/GCP/Azure ML services.

• Monitor and optimize model performance (latency, accuracy, hallucination rates).

• Use MLflow / Weights & Biases for experiment tracking and versioning.
• Stay updated with the latest research papers and open-source tools in AI/ML.

• Contribute to code reviews, technical documentation, and best practices. Required Skills & Qualifications
• Strong in Python (NumPy, Pandas, Scikit-learn, PyTorch, TensorFlow).

• Solid understanding of NLP and LLM architectures (Transformers, BERT, GPT, LLaMA, Mistral).

• Practical experience with vector databases (Pinecone, or FAISS or PgVector).

• Basic Knowledge with MLOps tools – Docker, Kubernetes, MLflow, CI/CD.

• Basic Knowledge of cloud platforms (AWS Sagemaker, GCP Vertex AI, or Azure ML).

• Good grasp of linear algebra, probability, statistics, optimization.

• Strong debugging, problem-solving, and analytical skills.

• Familiarity with Agile methodologies (Scrum, Jira, Git). Nice-to-Have Skills

• Experience with RLHF pipelines.

• Open-source contributions in AI/ML. Soft Skills

• Strong communication – able to explain AI concepts to technical & non-technical stakeholders.

• Collaborative – works well with product, design, and engineering teams.

• Growth mindset – eager to learn new AI techniques and experiment.

• Accountability – able to deliver end-to-end model pipelines with minimal supervision.

• Can works in a team. What We Offer

• Work on cutting-edge AI projects with real-world enterprise impact.

• Exposure to LLMs, reinforcement learning, and agentic AI.

• Collaborative Startup & Service culture with room for fast growth.

• Competitive compensation + performance-based incentives.

To Apply:

Please send your resume and cover letter to

We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, or any other characteristic.

Job Summary :

We are seeking a highly skilled and mathematically grounded Machine Learning Engineer to join our AI team. The ideal candidate will have 5+ years of ML experience with a deep understanding of machine learning algorithms, statistical modeling, and optimization techniques, along with hands-on experience in building scalable ML systems using modern frameworks and tools.

Key Responsibilities :

• Design, develop, and deploy machine learning models for real-world applications.

• Collaborate with data scientists, software engineers, and product teams to integrate ML solutions into production systems.

• Understand the mathematics behind machine learning algorithms to effectively implement and optimize them.

• Conduct mathematical analysis of algorithms to ensure robustness, efficiency, and scalability.

• Optimize model performance through hyperparameter tuning, feature engineering, and algorithmic improvements.

• Stay updated with the latest research in machine learning and apply relevant findings to ongoing projects.

Required Qualifications :

Mathematics & Theoretical Foundations :

• Strong foundation in Linear Algebra (e.g., matrix operations, eigenvalues, SVD).

• Proficiency in Probability and Statistics (e.g., Bayesian inference, hypothesis testing, distributions).

• Solid understanding of Calculus (e.g., gradients, partial derivatives, optimization).

• Knowledge of Numerical Methods and Convex Optimization.

• Familiarity with Information Theory, Graph Theory, or Statistical Learning Theory is a plus.

Programming & Software Skills :

• Proficient in Python (preferred), with experience in libraries such as : NumPy, Pandas, Scikit-learn, Matplotlib, Seaborn

• Experience with deep learning frameworks : TensorFlow, PyTorch, Keras, or JAX

• Familiarity with ML Ops tools : MLflow, Kubeflow, Airflow, Docker, Kubernetes

• Experience with cloud platforms (AWS, GCP, Azure) for model deployment.

Machine Learning Expertise :

• Hands-on experience with supervised, unsupervised, and reinforcement learning.

• Understanding of model evaluation metrics and validation techniques.

• Experience with large-scale data processing (e.g., Spark, Dask) is a plus.

Preferred Qualifications :

• Master's or Ph.D. in Computer Science, Mathematics, Statistics, or a related field.

• Publications or contributions to open-source ML projects.

• Experience with LLMs, transformers, or generative models.

What You'll Do

• Research & Experimentation: Explore and implement state-of-the-art ML techniques in computer vision and generative AI to improve our culling, editing, and face-matching models.
• Model Development: Train, fine-tune, and deploy models for blur detection, blink detection, duplicate detection, aesthetics scoring, and personalized editing.
• Benchmarking: Analyze current FilterPixel ML models against competitor offerings and identify opportunities for improvement in speed, accuracy, and reliability.
• Data Pipelines: Work with large-scale photo datasets to prepare, label, augment, and optimize data for training.
• Production Readiness: Build models that are not only accurate but also efficient and production-friendly (low latency, lightweight).
• Collaboration: Work closely with the product and engineering teams to convert research into real features photographers love.

What We're Looking For

• Experience: 1–3 years of experience in machine learning research projects (internships, academic research, or applied industry work all count).
• Strong Fundamentals: Solid understanding of deep learning, computer vision, and ML research techniques.
• Hands-On Skills: Proficiency in Python and ML frameworks (PyTorch, TensorFlow, JAX).
• Project Work: Prior research projects, papers, or contributions to ML/AI projects (academic or industry).
• Problem-Solving: Ability to take ambiguous problems, run experiments, and arrive at practical solutions.
• Nice-to-Have: Experience with CLIP, diffusion models, photo aesthetics models, or GANs.

Job Types: Full-time, Permanent

Pay: Up to ₹600,000.00 per year

Application Question(s):

• How many years of experience do you have in machine learning research projects?(internships, academic research, or applied industry work all counts).

Work Location: In person

About

is an AI Studio for Business, delivering deep science and real-world impact through cutting-edge AI solutions. We specialize in Agentic AI, Deep Learning, and full-stack AI-first product development across Healthcare, Pharma, BFSI, and Hi-Tech industries.

Ready to take a Rocket Leap with Science?

Role Overview:

We are seeking a high-potential
Machine Learning Engineer
with 2–4 years of experience and a strong academic background to join our AI innovation team. You'll design and implement ML models that drive intelligent automation and AI-first products for enterprise-grade use cases.

Key Responsibilities:

• Design, develop, and optimize machine learning models for NLP, computer vision, or multi-modal applications
• Build scalable pipelines for data preprocessing, training, and evaluation
• Translate academic research and ideas into efficient, production-ready implementations
• Explore and research relevant papers to propose plausible approaches for solving business problems
• Apply optimization techniques such as quantization, pruning, and distillation
• Collaborate with product, engineering, and domain teams to align ML systems with business needs
• Stay updated with the latest developments in ML frameworks, tooling, and deployment strategies

Required Skills & Expertise:

• Strong knowledge of Linear Algebra, Probability, and Statistics
• Machine Learning Proficiency:
  Hands-on experience with supervised, unsupervised, and deep learning methods (CNNs, RNNs, Transformers)
• Model Training Workflows:
  Familiarity with training-validation pipelines, k-fold cross-validation, early stopping, and hyperparameter tuning
• Evaluation Metrics:
  Experience evaluating models using confusion matrix, PR curves, F1 score, accuracy, and ROC-AUC
• Frameworks:
  Proficient in PyTorch with strong coding practices (clean, modular, testable)
• Tools:
  Experience with model libraries and hubs such as Hugging Face Transformers, TorchVision, etc.
• Data Handling:
  Ability to manage large datasets, custom data loaders, and data augmentation workflows
• Deployment:
  Experience with model packaging (TorchScript, ONNX) and serving (FastAPI, TorchServe)
• Experimentation & Reproducibility:
  Familiarity with tools like Weights & Biases, MLflow, or equivalent

Desirable Skills:

• Experience with seq2seq models, LLM fine-tuning, or multi-modal architectures
• Hands-on with knowledge distillation and other model compression techniques
• Exposure to Agentic AI or orchestration frameworks like LangChain or LangGraph
• Understanding of vector databases, embedding stores, and RAG pipelines
• Contributions to open-source ML projects or academic research
• Cloud AI Platforms:
  Preference for experience with
  Amazon SageMaker
  ,
  Azure AI Services
  ,
  Google Vertex AI
  , or
  Azure AI Foundry

What We Look For:

• Strong foundations in mathematics, algorithmic thinking, and ML system design
• Curiosity-driven mindset and a passion for solving complex problems
• Ability to convert ideas into scalable and efficient solutions
• Desire to learn and grow in a high-performance, innovation-first environment
• Clear communication and collaborative problem-solving skills

Required Education Background:

• B.Tech / M.Tech / MS from
  IISc, IITs, Top NITs, BITS Pilani, IIIT-H, DTU, NSUT
  , or equivalent Tier-1 institutions.

Why Join

Be part of the growth journey of

• Work in an AI-driven company that is making a real business impact.
• Enjoy a dynamic, fast-paced environment with career growth opportunities.
• Apply now and be part of our journey to shape the future of AI

ML Engineer – Indus AI (Full-time / Final-Year with NOC)

Location: On-site, Noida

We're looking for hands-on ML Engineers who can take models from research → code → production. You'll read and implement ideas from papers, fine-tune & optimize models, and deploy real-time systems.

Tracks (choose one or more):

Voice Models → TTS/STT, voice cloning, vocoders, diffusion pipeline

s. Computer Vision → ViTs, embeddings, recognition, detection/segmentatio

n. Agentic Systems → LLM orchestration, tool-using agents, RAG, LoRA/PEF

T

.

What You'll Do: End-to-end ownership: data → training → evaluation → deploymen

t.Ship production-grade PyTorch code with metrics & dashboard

s.Work closely with infra/product for real-time performanc

e

.

What We Nee

d:Strong ML & math fundamental

s.Ability to read/implement research paper

s.Understanding of LLM internals (tokenization, attention, finetuning vs pretraining

)

.

AI

Job Title: AI-ML Engineer

Location: Noida / Onsite

Experience: 2 – 4 years

Qualification: M.Tech or equivalent in AI / Machine Learning / Computer Science (IIT/NIT preferred)

Work Schedule: 5-day work week

We are currently hiring for an AI-ML Engineer position on behalf of a leading client in the AI space. The role involves leading the design and deployment of intelligent systems that power innovative products, including a personalized learning and assessment platform and an AI meeting-intelligence assistant. If you have deep expertise in machine learning, large language models (LLMs), agentic AI, and scalable system design, this opportunity allows you to build end-to-end pipelines that transform how humans and machines co-create knowledge.

This is a good chance to work with a visionary client driving the future of AI-powered solutions.

As an AI-ML Engineer, your responsibilities would be:

• Design and deploy AI models for NLP, speech, and multimodal tasks using TensorFlow / PyTorch.
• Develop and scale LLM pipelines using LangChain, AutoGen, and RAG architectures for multilingual summarization, Q&A, and personalized learning.
• Build and optimize APIs and services in Python (Flask / FastAPI) integrated with PostgreSQL and AWS.
• Collaborate with product and DevOps teams to ensure smooth CI/CD, containerization, and scalable deployment using Docker & Kubernetes.
• Enhance the adaptive learning pipeline with agentic reasoning, dynamic feedback loops, and user personalization logic.
• Improve the meeting-intelligence system by integrating advanced speech-to-text, biometric identification, and decision-tagging models.
• Research and implement new techniques in AI model compression, fine-tuning, and multimodal fusion (audio-text-image).
• Mentor junior AI engineers and contribute to code reviews, system architecture, and documentation.

Requirements

• 2– 4 years of applied ML experience building real-world products.
• Strong proficiency in Python, TensorFlow, PyTorch, and Scikit-learn.
• Hands-on with LangChain / LlamaIndex / AutoGen, RAG pipelines, and LLM integration (OpenAI, Gemini, Hugging Face).
• Proven experience with NLP, Speech Recognition, and Time-Series Forecasting.
• Solid understanding of MLOps / LLMOps (Docker, Kubernetes, CI/CD, AWS/GCP).
• Familiarity with PostgreSQL, REST APIs, and Flask / FastAPI frameworks.
• M.Tech or equivalent in AI / Machine Learning / Computer Science (IIT/NIT preferred).

Benefits

• Play a key role in designing and deploying intelligent systems for flagship, world-built products.
• Work with cutting-edge technologies like LLMs, agentic AI, RAG architectures, and multimodal fusion.
• Be instrumental in creating a culturally intelligent system that learns, adapts, and acts with purpose across diverse use cases
• Opportunity to mentor junior engineers and influence system architecture.
• Contribute to products that redefine human-AI collaboration in personalized learning and meeting intelligence.

Job Title:
Machine Learning Engineer (Fresher / Entry-Level)

Location:
Noida sec 63

Job Type:
Full-time

Experience:
0–1 year (Freshers Welcome)

Job Summary:

We are looking for a highly motivated and passionate Machine Learning Fresher to join our growing AI/ML team. As an ML Engineer, you will work on real-world projects involving data preprocessing, model training, and deployment. This is an excellent opportunity for someone eager to launch their career in Machine Learning and Data Science.

Key Responsibilities:

• Assist in building, training, and evaluating machine learning models.
• Work on data cleaning, preprocessing, and feature engineering.
• Collaborate with data scientists and software engineers on ML pipelines.
• Support the deployment and monitoring of models in production environments.
• Research and implement state-of-the-art ML algorithms under guidance.
• Document work clearly and contribute to project reports or presentations.

Requirements:

• Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, or related field.
• Solid understanding of ML concepts (supervised/unsupervised learning, regression, classification, etc.).
• Programming experience in Python and ML libraries (e.g., Scikit-learn, NumPy, Pandas, TensorFlow/PyTorch – basic level is fine).
• Basic knowledge of data structures, algorithms, and software development principles.
• Familiarity with Jupyter notebooks, Git, and version control.
• Strong analytical and problem-solving skills.
• Eagerness to learn and apply new technologies.