About the role

Senior AI Engineer — Technology & Product

About AMPYR GTC

AMPYR Energy’s Global Technology Centre (GTC), based in New Delhi, is a centralized hub of expertise and support for renewable energy projects across the globe. Our mission is to drive innovation, efficiency, and sustainability across the organization’s global operations in solar and renewable energy sectors. AMPYR GTC works closely with AMPYR’s renewable energy platforms in Europe and the USA, providing leading-edge expertise on engineering & design, procurement, investment analysis, capital raise and business development.

Role Overview

The Senior AI Engineer is AMPYR GTC’s hands-on builder for applied AI — writing the production code that turns Large Language Models, Retrieval-Augmented Generation, and domain-adapted models into reliable internal tools for analysts, engineers and decision-makers. You will work directly with the GM — Product & Technology, who owns the AI strategy and architectural direction; your job is to execute, challenge, and ship. You will pair closely with a Full-Stack Developer on the team and collaborate with domain experts across our international platforms. This is a foundational individual-contributor hire — the production systems you build in your first year will shape AMPYR’s AI capability for the next several.

Key Responsibilities

Build production Retrieval-Augmented Generation (RAG) pipelines over enterprise document and data corpora — hands-on, end-to-end Python development covering ingestion, chunking, embedding, retrieval, re-ranking, and evaluation.
Ship domain-adapted LLM capabilities through prompt engineering, fine-tuning, or adapter-tuning techniques, with measurable improvements in task accuracy and cost.
Build evaluation, monitoring, and guardrail systems for LLM-based applications — hallucination detection, citation validation, drift monitoring, and continuous quality measurement.
Implement LLM features inside internal web applications — writing the Python and API code that integrates inference, retrieval, and tool-use into user-facing workflows.
Make and present build-vs-buy recommendations between hosted inference providers and self-hosted models, supported by cost, accuracy, latency, and data residency analysis.
Pair with another engineer on the team through code review, architecture walk-throughs, and joint debugging. This is peer collaboration, not line management.
Work directly with domain experts to translate subject-matter workflows into AI-assisted tooling that demonstrably reduces manual effort and improves decision quality.
Set up MLOps foundations — model versioning, eval pipelines, prompt management, and deployment workflows. You will build these yourself, not specify them for others to build.
Track AI infrastructure costs continuously and surface trade-offs before they become budget surprises.
Contribute hands-on to forward-looking initiatives such as forecasting and optimisation engines as the platform expands.

Qualifications, Experience and Skill Set

Qualification
- Bachelor’s degree in Computer Science, Engineering, Mathematics, Statistics, or a related quantitative field. Master’s degree welcomed but not required.
- Relevant certifications (AWS / Azure AI, MLOps, model deployment) considered favourably.
Years of experience
- 4–6 years total professional experience in software engineering, machine learning, or data science.
- Minimum 2 years of production experience with Large Language Models — building, deploying, and maintaining LLM-based applications.
Must-have skill set
- Strong proficiency in Python and the modern AI / ML ecosystem (LangChain, LlamaIndex, Hugging Face, or equivalents).
- Hands-on experience designing and deploying production RAG systems — chunking strategy, hybrid retrieval, re-ranking, evaluation, and hallucination handling.
- Practical experience with foundation model APIs (OpenAI, Anthropic Claude, Azure OpenAI, AWS Bedrock) and at least one open-weight model family (Llama, Mistral, or similar).
- Experience with vector databases (Chroma, Pinecone, pgvector, Weaviate, or similar) and embedding model selection trade-offs.
- Strong understanding of prompt engineering, function calling / tool use, and agentic patterns.
- Production experience with Python web frameworks (FastAPI or Flask), PostgreSQL, Docker, and AWS (or equivalent cloud).
- Demonstrated ability to make build-vs-buy decisions on AI infrastructure with cost discipline.
- Excellent analytical skills with the ability to interpret complex data and reason about model behaviour, error modes, and failure cases.
Good-to-have skill set
- Experience fine-tuning or adapter-tuning foundation models (LoRA, QLoRA, SFT, instruction tuning).
- Exposure to MLOps tooling (MLflow, Weights & Biases, LangSmith, Langfuse, or similar).
- Experience with document intelligence workflows — OCR, layout-aware parsing, tabular extraction (Docling, Unstructured, Textract).
- Familiarity with the energy, infrastructure, or financial services domain.
- International or cross-cultural collaboration experience — comfort working with stakeholders in the UK, Europe, and the USA across time zones.
- Open-source contributions, technical writing, or conference presentations in the applied AI space.
Communication and leadership
- Strong communication and presentation abilities — able to explain architectural choices and trade-offs clearly to non-technical leadership and domain experts.
- Comfortable pairing with and informally coaching a junior engineer through code review and joint problem-solving; formal people-management experience is not required and is not part of this role.
- Comfort working independently in a small, senior team without a heavy management layer.
- Bias toward shipping working software over producing documentation about software.

Location and Work Arrangement

AMPYR GTC, New Delhi.

Hybrid working: 4 days in-office, Wednesdays work-from-home, per AMPYR group policy.

Reporting

Reports to: GM — Product & Technology, AMPYR GTC.