Machine Learning Engineer – Generative AI (LLMs / RAG / Agentic AI)

Role Summary

Key Responsibilities

• Develop and optimize AI systems leveraging **LLMs, RAG, and agentic AI frameworks** (LangChain, LangGraph).
• Build and deploy **production-grade ML pipelines** with real-time inference and retrieval components.
• Design and manage APIs and streaming services to integrate AI models into enterprise platforms.
• Implement containerized, orchestrated deployments using **Docker, Kubernetes, and Azure ML**.
• Automate data preprocessing, model training, evaluation, and versioning pipelines.
• Collaborate with cross-functional teams to integrate models into front-end, analytics, and automation workflows.
• Ensure governance, compliance, and security of deployed AI workloads.
• Conduct performance benchmarking and optimize inference latency and cost.
• Monitor AI systems in production using observability frameworks (logging, metrics, tracing).
• Participate in architecture discussions to enhance scalability and reliability of AI services.

Required Skills & Experience

• Strong hands-on experience with **LLMs, RAG, and agentic frameworks** (LangChain, LangGraph, Semantic Kernel, etc.).
• Proficiency in **Python**, with deep understanding of ML libraries like **PyTorch, TensorFlow, scikit-learn, Hugging Face Transformers**.
• Solid experience in **API and microservices engineering** (FastAPI, Flask).
• Familiarity with **streaming architectures** and real-time data handling.
• Knowledge of **cloud platforms (Azure preferred)**, including Azure AI, Cognitive Services, and ML Ops.
• Experience with **containerization and orchestration** (Docker, Kubernetes).
• Understanding of **vector databases** (Pinecone, Weaviate, FAISS) and retrieval mechanisms.
• Experience in CI/CD, model deployment, and production monitoring.

Preferred Skills

• Exposure to **GPU-based inference optimization** and **serverless deployment**.
• Knowledge of **observability and monitoring tools** for AI (Prometheus, Grafana, Azure Monitor).
• Experience in **model fine-tuning**, **prompt engineering**, or **agentic orchestration**.
• Understanding of **AI governance, ethical AI, and data privacy** frameworks.

Soft Skills

• Strong analytical and problem-solving mindset.
• Excellent collaboration and communication skills.
• Passion for innovation, experimentation, and applied AI.