Role Purpose
As 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 Requirements
- Willingness 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.
