Forward Deployed Engineer/ Agentic AI Solutions engineer

Job Description: AI Solutions Engineer

Bloom Energy faces an unprecedented opportunity to change the world and the way energy is generated and delivered.  Our mission is to make clean, reliable energy affordable globally.  Bloom’s Energy Server delivers highly reliable and resilient, always-on electric power that is clean, cost-effective, and ideal for micro grid applications.  We are helping our customers power their operations without disruption and combustion.

We are looking for AI Solutions Engineer to join our team in one of today’s most exciting technologies.  This role would report to the Business Intelligence Manager and based in Bengaluru / Mumbai, India.

The AI Solutions Engineer will support the Services Business Intelligence team to develop AI/ML solutions and automation into our service strategy. This will include building and deploying machine learning algorithms (for applications to support predictive maintenance and process optimization) and developing AI solutions (agents), process automations and enhanced analytics. 

Requirements:

·       2-4 years of hands-on experience building AI/ML solutions in a production or near-production environment

·       Strong proficiency in Python – you write clean, production-grade code, not just notebooks

·       Working experience with LLMs and generative AI: prompt engineering, RAG architecture, agentic workflows, or conversational AI systems

·       Strong SQL skills and experience working with relational databases and data warehouses

·       Experience with cloud platforms (AWS strongly preferred – Bedrock, Lambda, S3, Redshift, ECS/EKS)

·       Familiarity with at least one orchestration/agentic framework: LangChain, LanGraph, CrewAI, AuguGen, or similar

·       Understanding of data modeling, ETL/ELT pipelines, and working with messy, imperfect enterprise data

·       Strong communication skills – ability to explain technical concepts to non-technical stakeholders

Strong Plus:

·       Experience with vector databases and knowledge graphs

·       Hands-on experience with AWS Bedrock

·       Experience building multi-system integrations across enterprise platforms

·       Exposure to AI governance: audit logging, RBAC, confidence scoring, human-in-the-loop workflows, hallucination prevention

·       Experience with MCP or similar tool-use protocols

Responsibilities:

Agentic AI Development

·       Design, build, and iterate on agentic AI workflows using frameworks such as LanGraph, CrewAI, or equivalent orchestration tools

·       Develop retrieval-augmented generation (RAG) pipelines that ground LLM responses in approved enterprise data sources with source citation, confidence scoring, and traceability

·       Build and maintain multi-agent systems that decomposes complex operational questions into sub-tasks, query structured/unstructured data, and synthesize actionable outputs

·       Implement prompt engineering, prompt libraries, and evaluation harnesses to ensure response quality, accuracy, and governance compliance

·       Integrate with enterprise LLMs (e.g. AWS Bedrock / Anthropic Claude, OpenAI) and manage model abstraction layers for flexibility

Enterprise Data Integration & Engineering

·       Build connectors and data pipelines to ingest and query data across different operational systems

·       Develop and maintain semantic layers, knowledge graphs, and vector databases for contextual retrieval

·       Implement data quality checks, trusted-source hierarchies, and conflict resolution logic when multiple systems provide contradictory data

·       Support MCP or equivalent server integrations for live system connectivity

ML/AI Solutions & Analytics

·       Build and deploy ML models for predictive maintenance, performance optimization, anomaly detection, and operational forecasting

·       Develop automated reporting, dashboards, and self-service analytics tools

·       Apply statistical methods to identify patterns, root causes, and actionable insights from fleet and service data

Collaboration & Scaling AI Adoption

·       Partner with cross-functional teams to translate operational pain points into AI-enabled solutions

·       Contribute to the Agent Factory – building reusable connectors, templates, governance patterns, and deployment blueprints that scale across future service AI agents

·       Document architecture, data flows, and solution designs for knowledge transfer and production handoff

·       Support vendor collaboration and POC evaluation for enterprise AI partnerships

Graduate Education in:

·       BA/BS/MA in Computer Engineering, Computer Science, Data Science or equivalent