AI/ML Engineer

Role PurposeAs an AI/ML Engineer, you will bridge the gap between cutting-edge research and production-grade software. You will be responsible for architecting and deploying "Agentic" systems and Generative AI solutions that solve complex business challenges, moving beyond simple automation to create intelligent, self-optimizing workflows.

Requirements:Education: Bachelor’s degree in CS, AI, Data Science, or Computational Linguistics (Master’s in AI/ML preferred).2–3 years in professional software development with a focus on AI integration.Strong grasp of LLMs, RAG, Autonomous Agents, and Prompt Engineering. Familiarity with MCP (Model Context Protocol) is highly valued.

The Stack:Hands-on experience with LangChain, LangGraph, and FastAPI.Proficiency in AWS (Bedrock, Q, SageMaker) or equivalent (Vertex AI, Azure AI Foundry).Experience with LLM monitoring and evaluation tools like LangSmith or similar. Proficiency in Vector DBs (OpenSearch, pgvector) and NoSQL/Relational systems (DynamoDB).GCP Professional ML Engineer or DeepLearning.AI specializations are a plus.

Non-Technical RequirementsWillingness to visit customer sites to understand pain points and present technical solutions.Willingness to work from the office to foster high-bandwidth collaboration with the engineering team.Ability to articulate complex AI concepts to both technical peers and non-technical stakeholders or clients.

Responsibilities:Design and deploy scalable AI-driven solutions (RAG, Agentic workflows) to optimize internal workflows and elevate customer experiences.Build and maintain the next generation of AI tools, including multi-turn conversational agents, autonomous automation scripts, and intelligent middleware.Monitor, evaluate, and fine-tune LLMs and traditional ML models to ensure sustained accuracy, low latency, and relevance in production.Leverage AI for deep-dive data analytics, including anomaly detection, predictive forecasting, and automated insight generation across structured and unstructured datasets.Partner with Sales, Pre-Sales, and Product teams to translate business requirements into technical AI roadmaps and ensure seamless infrastructure integration.Develop robust integrations between AI systems and diverse data sources, ensuring high-performance retrieval from relational and vector databases.