Engineer II

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About The Role

Senior AI & Machine Learning Engineer

Production Environments.

Key Responsibilities

• Design, train, and optimize ML models for prediction, classification, and analytical use cases.
• Build, fine-tune, and deploy open-source LLMs (GPT, Llama, Mistral, T5, Falcon, BERT, ViT).
• Apply prompt engineering and build RAG-based and multi-Agent solutions.
• Develop agentic AI workflows using LangChain, LangGraph, CrewAI, Autogen, or PhiData.
• Deploy and manage models using MLflow, Kubeflow, SageMaker, Docker, and Kubernetes.
• Monitor model performance, drift, and ensure scalability and robustness in production.
• Collaborate with cross-functional teams to align ML solutions with business and product goals.
• Document model architecture, workflows, and experiments.
  

Required Skills & Qualifications

• 6-8 Years of overall development experience
• 4 years of experience in ML engineering or GenAI development.
• Proven experience in deploying ML/GenAI Agents in production at scale.
• Strong proficiency in Python and Java preferred.
• Hands-on with TensorFlow, PyTorch, Scikit-learn, Hugging Face Transformers, LangChain.
• Integrate AI services into existing web and workflow applications using Python (FastAPI), REST APIs and secure connectors.
• Experience fine-tuning open-source LLMs and developing generative or agentic AI systems.
• Knowledge of data processing tools (SQL, Spark, Pandas, NumPy).
• Deep Knowledge of LLM orchestration, Vector databases (FAISS, Chroma, Pinecone etc.)
• Excellent problem-solving and communication skills.
  

Preferred Skills

• Knowledge of cloud platforms (AWS, GCP, Azure).
• Understanding of AI governance, ethical AI, and model interpretability frameworks.
• Familiarity with reinforcement learning or continuous learning pipelines.
• Experience with Git and SDLC best practices.
• Certifications in Azure/AWS AI is a plus.
  

Summary

machine learning

generative AI

agentic automation