Engineer II

Job Description

Job Title: Senior AI & Machine Learning Engineer Experience: 6-8 Years Domain: Machine Learning / Generative AI / Agentic Frameworks About The Role We are looking for a Senior AI & Machine Learning Engineer with deep expertise in machine learning, generative AI, and agentic AI workflows. The role involves building, fine-tuning, and deploying large-scale ML and GenAI models while developing intelligent Enterprise-grade agentic systems that operate autonomously in 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 This role blends advanced machine learning , generative AI , and agentic automation to deliver production-ready, scalable AI solutions. The engineer will work across the full AI lifecycle from model development to deployment within a high-performance AI/ML domain team.