Job
Description
Role Overview: You are sought after to be a hands-on AI/ML engineer responsible for designing, building, and producing Generative AI solutions including RAG pipelines and multi-agent systems to automate workflows and drive operational excellence. You will collaborate closely with solution/data architects, software developers, data engineers, and domain experts to rapidly prototype and deliver scalable, enterprise-grade systems. This individual contributor role requires strong research skills, deep expertise in AI foundation models, and the ability to translate cutting-edge concepts into impactful solutions for digital grid challenges. Key Responsibilities: - End-to-End GenAI Development: Design and implement RAG pipelines, agentic workflows, and LLM integrations for tasks such as document understanding, classification, and knowledge assistance. - Multi-Agent Orchestration: Build agent-based applications for planning, tool use, and execution using frameworks like LangGraph, Semantic Kernel, and prompt orchestration tools. - AI Enterprise Architecture: Strong Experience in AI architecture (scalable, modern, and secure) design across AI/ML enterprise solutions. - Data & MLOps Foundations: Architect data pipelines and cloud solutions for training, deployment, and monitoring on Azure/AWS with Docker, Kubernetes, and CI/CD. - Rapid Prototyping to Production: Convert problem statements into prototypes, iterate with stakeholders, and harden into production-ready microservices (FastAPI) with APIs and event-driven workflows. - Evaluation & Reliability: Define rigorous evaluation metrics for LLM/ML systems (accuracy, latency, cost, safety), optimize retrieval quality, prompt strategies, and agent policies. - Security & Compliance: Implement Responsible AI guardrails, data privacy, PII handling, access controls, and auditability. - Collaboration & Enablement: Partner with data engineers, mentor junior team members, and contribute to internal documentation and demos. Qualifications Required: - Education: Bachelors/masters in computer science, Data Science, Engineering, or equivalent experience - Experience: - 7-12 years delivering AI/ML, Data Science solutions in production. - 2-3 years focused on Generative AI/LLM applications. - Technical Skills: - Programming: Strong Python (typing, packaging, testing), data stacks (NumPy, Pandas, scikit-learn), API development (FastAPI/Flask). - GenAI Expertise: - Prompt engineering, RAG design (indexing, chunking, reranking). - Embeddings and vector databases (FAISS, Azure AI Search, Pinecone). - Agent frameworks (LangGraph, Semantic Kernel) and orchestration strategies. - Model selection/fine-tuning, cost-performance optimization, safety filters. - Cloud & Data: Hands-on with Azure/AWS; experience with Azure OpenAI, Azure AI Search, Microsoft Fabric/Databricks (preferred), Snowflake or similar DWH. - MLOps: Docker, Kubernetes, CI/CD (GitHub Actions/Gitlab), model deployment/monitoring. - Architecture: Microservices, event-driven design, API security, scalability, and resilience. - Soft Skills: - Excellent team player with the ability to work collaboratively in cross-functional and multicultural teams. - Strong communication skills able to explain complex technical ideas to non-technical stakeholders. - Adaptability to changing priorities and evolving technologies. - Problem-solving mindset with creativity, curiosity, and a proactive approach. - Time management & prioritization in fast-paced, iterative environments. - A mentoring attitude toward junior colleagues and an openness to receiving feedback. - Strong sense of ownership and accountability over deliverables. - Domain Knowledge: Experience applying AI/ML to power systems, electrical grids, or related domains.,
