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

Skills
Python, AWS, SQL, Machine Learning, Data Modeling, ETL, LLM

Experience
2-4 yrs