Ai Ml Engineer

Looking for strong AIML ENgineer.

Key Responsibilities

• Design, develop, and maintain AI-powered applications and platform services.
• Build and enhance Generative AI solutions using enterprise-approved LLMs and AI frameworks.
• Develop scalable REST APIs, microservices, and backend services supporting AI workloads.
• Integrate AI services with enterprise platforms, governance controls, observability, and security frameworks.
• Collaborate with Product, Engineering, Architecture, UX, Security, and Data teams to deliver production-grade AI capabilities.
• Implement model evaluation, prompt engineering, experimentation, and AI quality measurement frameworks.
• Contribute to AI Platform products including evaluation services, AI agents, AI governance integrations, model routing, and data protection services.
• Participate in cloud deployment, operational support, performance optimization, and troubleshooting activities.
• Support CI/CD automation and infrastructure-as-code initiatives.
• Ensure solutions meet enterprise requirements for scalability, security, reliability, compliance, and observability.

Must-Have Skills

• 6+years of Software engineering experience.

• 2+ years of hands on Generative AI or LLM application development experience.

• Strong proficiency in Python.
• Experience building APIs and microservices in cloud environments.

• Hands-on experience with LangChain, RAG, vector search, and/or prompt engineering.

• Experience deploying applications to Kubernetes.
• Strong understanding of software design patterns and scalable architecture.
• Experience with SQL and NoSQL databases.
• Familiarity with Git-based development workflows and CI/CD pipelines.
• Strong problem-solving and communication skills.
• Ability to work in Agile/Scrum environments.

Tech Stack:

Python, Large Language Models (OpenAI, Anthropic, Gemini, Llama) , Retrieval-Augmented Generation (RAG), Prompt Engineering, AI Agents / Agentic Workflows , Model Evaluation Frameworks ,Embeddings and Semantic Search, Google Cloud Platform (GCP) , Vertex AI , Kubernetes (GKE preferred) , Docker , Cloud Storage , API Gateway technologies, FastAPI, Microservices Architecture , Event-Driven Architecture, Kafka, MongoDB, GitHub, GitHub Actions, CI/CD Pipelines, OpenTelemetry