Sr. Engineer - AI/ML

Location: Sahibzada Ajit Singh Nagar, India

Department: AI-DS

Experience: 5-8

Skills: Prompt Engineering, Python, Machine Learning / Deep Learning, LangChain / LangGraph, Hugging Face Transformers, Large Language Models (LLMs), AI Agents / Agentic AI Systems, Retrieval-Augmented Generation (RAG), PyTorch / TensorFlow

• Design and develop end-to-end AI/ML solutions including data pipelines, model training, deployment, and monitoring.
• Build LLM-powered applications using transformer models (GPT, BERT, LLaMA, Mistral).
• Implement Retrieval-Augmented Generation (RAG) systems with embeddings, semantic search, and vector databases.
• Optimize models through fine-tuning, prompt engineering, quantization, and inference optimization.

• Design and implement multi-agent architecture using frameworks such as LangGraph, CrewAI, Haystack, AutoGen, or custom orchestration layers.
• Build tool-calling workflows, API integrations, and autonomous decision pipelines.
• Implement agent reasoning, planning, memory management, and evaluation frameworks.
• Develop LLM guardrails, safety checks, and performance evaluation pipelines.

• Build production-grade AI systems with monitoring, observability, and reliability.
• Develop MLOps pipelines for model training, versioning, deployment, and lifecycle management.
• Implement CI/CD pipelines, experiment tracking, and automated testing for ML workflows.

• Deploy AI systems on AWS using services such as SageMaker, Bedrock, Lambda, Step Functions, API Gateway.
• Work with data services including S3, DynamoDB, Aurora, OpenSearch, Glue, and Athena.
• Manage containerized environments using Docker, Kubernetes, ECS/EKS and IaC tools such as Terraform or CDK.

• Lead, mentor, and grow a team of ML engineers, data scientists, and research engineers.
• Conduct code reviews, provide technical guidance, and promote best practices in ML development, AI architecture, and MLOps.
• Guide the team in building scalable AI agent systems and LLM-based applications.

• Work with leadership and cross-functional teams to define AI strategy, roadmap, and key success metrics.
• Translate business needs into actionable technical plans and ensure timely, high-quality execution.
• Create architectural documents, technical proposals, and progress reports for stakeholders.

• • 5–8 years of experience in AI/ML engineering or applied machine learning.
  • Strong expertise in machine learning, deep learning, and transformer architectures.
  • Hands-on experience with LLMs, embedding’s, prompt engineering, and RAG systems.
  • Experience building AI agents, autonomous workflows, or tool-augmented LLM applications.
  • Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, Hugging Face.
  • Experience building production ML systems and cloud-based AI platforms.
  • Excellent problem-solving, system design, and communication skills.

• • Certifications in AI/ML, cloud computing, or data engineering.