AI Engineer (Solar/Renewable)

Greeting from RecruitGuruz Staffing Solutions!

We are currently looking for AI Engineer with US based Solar MNC.

Our client accelerates United States solar, battery storage and EV projects by providing turnkey development as a service including 100% internal systems engineering. The company deploys a leading team that spun out of the largest solar power producer in the world.

Our client has fully commoditized renewable energy project development-as-a-service (DaaS), including 100% internal systems engineering and Construction-as-a-Service (CaaS). Our client support developers, builders, land-owners and financers through any portion of project execution, including turnkey delivery of built assets.

Location: Chennai / Delhi

Experience: 2-5 Years

Qualification: BE/B.Tech/ME/M.Tech/Any Graduate/Computer Science/AI/data science

Position Summary

Our client is building an AI-native way to develop renewable energy projects. We are looking for AI Engineers who combine strong, hands-on AI engineering skills with a working understanding of the energy business, and who want to see what they build used every day by engineers, developers, analysts, and finance teams.

Working within client's Automation team, you will partner with our business verticals (engineering, project development, permitting, GIS, finance, due diligence, procurement, and operations) to find

where AI can remove manual effort, build those solutions with the Automation team, take them through testing and deployment, and train each department to make them part of how they work. You will also help build and extend CIRs AI products, including EPSA, our AI-powered site due- diligence platform, and DaRA, our AI platform for data-room review and risk analysis in project M&A.

Core Responsibilities

Identify & Prioritize Opportunities:

• Work alongside department heads and teams to map how work gets done today and pinpoint repetitive, time-intensive, or error-prone tasks suited to AI or automation.
• Assess each use case on business value (hours saved, turnaround time, quality and risk reduction), technical feasibility, and data readiness, and maintain a prioritized AI roadmap for each vertical you support.
• Match the solution to the problem, choosing between a script, a workflow automation, a retrieval-based assistant, or a multi-step agent, and recommend against AI where a simpler approach works better

Design, Build & Deploy:

• Design and build AI-powered tools and agents with the Automation team, from prototype to production, including LLM workflows, document-intelligence pipelines, retrieval systems and agentic workflows with human review where accuracy is critical.
• Typical problem areas include preliminary CAPEX estimation, site and permitting research (parcels, zoning, AHJ requirements, applicable codes), GIS and environmental screening,
• engineering design review, contract and data-room review, and financial model checks.
• Integrate solutions with the systems teams already use (Microsoft 365, SharePoint, monday.com, internal databases and APIs) so new tools fit into existing workflows.
• Build evaluation datasets with domain experts, test accuracy against real project data and run user acceptance testing with each department before rollout.
• Deploy, monitor, and maintain solutions in production, tracking accuracy, usage, cost and failure modes and iterate based on user feedback.

Develop CIRs AI Products:

• Contribute to the development of EPSA, DaRA, and CIRs internal agent platform by building new agents, features and data integrations.
• Build reusable components (tools, prompts, data connectors, evaluation harnesses) so each new automation is faster and more reliable to deliver than the last.

Enable Teams & Drive Adoption:

• Train department users through hands-on sessions, documentation, and quick-reference guides and serve as the go-to AI contact for the verticals you support.
• Support CIRs company-wide AI adoption program, promote responsible use of AI tools and develop AI champions within each department.
• Measure adoption and impact after rollout (usage, time saved, quality) and report outcomes to department heads and leadership.

Model Strategy & Governance:

• Select and evaluate models (Claude, GPT, Gemini, and open-weight models) for each use case, balancing accuracy, latency, cost and data sensitivity.
• Apply guardrails, access controls and data-privacy practices suited to confidential client, project and financial data.
• Track developments in AI and bring practical, tested ideas back to the team.

Educational and Technical Skills:

• Bachelors or masters degree in computer science, AI/data science, or an engineering discipline (electrical, energy, mechanical, civil, or related). Engineers who have moved into AI and software development are encouraged to apply.
• Certifications in cloud, AI/ML, or renewable energy are a plus but not required.

Required Skills & Experience

AI Engineering:

• Hands-on experience building and shipping LLM applications to real users: prompt and context engineering, retrieval-augmented generation (RAG), tool/function calling and structured outputs.
• Experience building agentic workflows with LangGraph (our primary framework) or a comparable framework, including multi-step orchestration, state management, and human-in-the-loop review.
• Working knowledge of leading model APIs (Anthropic Claude, OpenAI, Google Gemini) and their trade-offs in accuracy, cost, latency, and context limits.
• Experience extracting structured information from unstructured documents such as PDFs, scanned reports, contracts, spreadsheets, and drawings.
• Ability to design evaluations that show whether an AI solution is accurate enough for business use.

Software & Data Engineering:

• Strong Python skills; experience building APIs (FastAPI or similar) and working with SQL databases (PostgreSQL preferred).
• Comfortable with Git, Docker, CI/CD, and at least one major cloud platform (AWS, Azure, or GCP).
• Experience building data pipelines and integrating with business systems and third-party APIs.
• Ability to build simple interfaces or internal tools (e.g., React, Streamlit) so solutions reach users quickly.

Energy Domain Knowledge:

• Working knowledge of the renewable energy project lifecycle (solar PV and/or battery storage) across one or more stages: site origination, engineering design, permitting, interconnection, financing, or due diligence.
• Ability to understand domain workflows and discuss problems credibly with engineers, project developers, and finance teams.

Experience:

• 3+ years of software, data, or ML engineering experience, including at least 1.5 years building LLM/GenAI applications used in production.
• Proven track record of taking at least one AI or automation solution from idea to sustained adoption by business users, beyond prototypes or demos.
• Experience gathering requirements directly from non-technical users and training them on new tools.

Good to Have

• Experience at a renewable energy developer, IPP, EPC, engineering consultancy, or energy software company.
• Familiarity with solar design and energy-modeling tools (PVsyst, pvlib, SAM) or BESS sizing.
• Geospatial skills: PostGIS, GDAL, GeoPandas, rasterio, QGIS/ArcGIS and raster/vector analysis for site assessment.
• Exposure to project finance concepts: CAPEX/OPEX estimation, LCOE, financial models and PPAs.
• Familiarity with the US renewable energy market, including county permitting and ISO/RTO interconnection processes.
• Experience with vector databases (e.g., pgvector), workflow orchestration (e.g., Airflow) and LLM observability tools (e.g., LangSmith, Langfuse).
• Experience with open-weight or self-hosted models (e.g., vLLM, Ollama), fine-tuning, or Model Context Protocol (MCP) integrations.
• Computer vision experience with satellite/aerial imagery or engineering drawings.
• Prior experience in a forward-deployed engineering, solutions engineering, or internal AI enablement role

If you are interested, please apply or send your cv on kirti.patel@recruitguruz.com

Regards,

Kirti Patel