Senior AI/ML Engineer

Basic Information

• Role Title:

  Senior AI/ML Engineer

• Required Technical Skillset

  : Python, Java/Scala/Golang, ML frameworks at least one (TensorFlow/PyTorch), MLOps, Microservices, Cloud at least one (Azure/AWS/GCP), LLM/Generative AI

• Work Location:

  Gurugram
• Work

  Experience

  : 5-7 years

About the Team and Role

AI Engineering and Solutions

Senior AI/ML Engineer

Key Responsibilities

ML Platform Development

• Architect and develop end-to-end ML pipelines for data ingestion, model training, and deployment at scale.
• Implement scalable and secure infrastructure on

  Azure/AWS/GCP

  using IaC (e.g., Terraform, CloudFormation).

Model Engineering & MLOps

• Collaborate with data scientists to productionize ML/DL models, including

  Generative AI/LLMs

  .
• Set up and manage automated CI/CD pipelines for model versioning, testing, deployment, and monitoring (e.g., MLflow, Kubeflow).
• Fine-tune large language models on domain-specific data and optimize for real-world scenarios (e.g., prompt engineering, RAG, agent-based architectures).

Microservices & API

• Develop high-performance microservices in

  Java/Scala/Golang

  or Python for real-time model inference.
• Containerize services using Docker and orchestrate them via Kubernetes for scaling and reliability.
• Integrate APIs that deliver low-latency, customer-facing AI features (e.g., chatbots, generative text/image solutions).

Performance & Scalability

• Optimize ML systems for

  low-latency

  applications and high-throughput services, particularly for LLM-based endpoints.
• Implement caching, load balancing, and auto-scaling strategies to handle large-scale traffic.

AI/LLM Innovation

• Stay current with state-of-the-art LLM, Generative AI, and agent-based technologies to drive continuous innovation.
• Experiment with advanced techniques (e.g., reinforcement learning from human feedback, multi-modal embeddings) to enhance platform capabilities.
• Customize GPT/LLM for specific use cases using Continued Pre-Training (CPT) or Instruction Fine Tuning (IFT).

Product & Business Enablement

• Work with product managers and business stakeholders to shape the AI/ML roadmap, ensuring alignment with strategic objectives.
• Translate complex technical solutions into clear business value propositions and drive ROI analysis for ML initiatives.

Collaboration & Cross-Functional Engagement

• Partner with engineering, data, and business teams to integrate AI/ML solutions seamlessly into the Tata Neu ecosystem.
• Provide technical mentorship, best practices, and leadership to junior engineers and data scientists.

Competencies for the Role

Educational Background

• B.Tech/BE/M.Tech or equivalent in Computer Science, Data Science, or a related field.

Technical Expertise

• Programming

  : Expert in Python and at least one of Java/Scala/Golang for building robust microservices.

• ML & LLM Frameworks

  : Hands-on experience with TensorFlow, PyTorch, or Scikit-learn; familiarity with LLM fine-tuning and associated libraries (e.g., Hugging Face).

• Cloud & DevOps

  : Proficiency in cloud platforms (Azure/AWS/GCP) and container orchestration (Kubernetes, Docker).

• Data Processing

  : Familiarity with Spark/Kafka or similar technologies for large-scale or real-time data workflows.

• Generative AI, Agents and RAG:

Experience of building applications with LangChain, LangGraph, LlamaIndex and similar frameworks and libraries.

• Exposure to Vector Databases, Embedding Models, and Semantic Similarity Search.
• Expertise in advanced Prompt Engineering and Retrieval Augmented Generation (RAG) techniques for working with structured and unstructured data.
• Experience in applying AI to practical and comprehensive technology solutions, and developing and deploying machine learning systems into production.

MLOps & Automation

• Experience in setting up CI/CD pipelines, model registries, and feature stores.
• Knowledge of best practices for model monitoring, logging, and drift detection.

Analytical & Problem-Solving Skills

• Ability to design experiments, interpret complex data, and create actionable insights.
• Familiarity with advanced statistical and ML techniques, including advanced NLP, LLM fine-tuning, and agent-based AI.

Product & Business Acumen

• Proven track record of delivering ML solutions that impact business metrics and user experience.
• Capable of balancing technical trade-offs with product requirements and ROI considerations.

Communication & Leadership

• Strong written and verbal communication skills for stakeholder alignment.
• Demonstrated ability to lead projects and mentor cross-functional teams.