Forward Deployed Engineer - GenAI

Systems Limited; India; On-site; INR 1,800,000 - 3,200,000; Full time

Job summary

Systems Limited is seeking a skilled GenAI/LLM engineer to build production-grade GenAI applications, integrating LLM APIs, and implementing robust RAG pipelines. You will design sophisticated retrieval and embedding strategies, fine-tune models, and collaborate closely with architects and data scientists to deliver enterprise-ready features.

The role requires 4–8 years in software development with 1–3 years in GenAI projects, strong Python, and experience with LangChain, vector stores, and API

Qualifications

• 4–8 years of software engineering experience, with 1–3 years hands-on GenAI/LLM application building.
• Strong Python programming skills.
• Experience with LangChain or LlamaIndex.
• Familiarity with vector databases and embedding strategies.
• Understanding LLM failure modes and mitigations.
• Experience with model fine-tuning techniques (LoRA/QLoRA).
• Hands-on with enterprise GenAI platforms (Azure AI Foundry, AWS Bedrock).
• API design, auth, rate limiting, and streaming responses.
• Clear technical writing and ability to document architectures.

Responsibilities

• Build GenAI applications — LLM-powered features, copilot/chat experiences, enterprise search.
• Design and implement RAG pipelines: chunking strategy, embedding selection, hybrid retrieval, re-ranking.
• Fine-tune and adapt models (LoRA/QLoRA) when needed.
• Engineer and version production prompts; build prompt management into the app layer.
• Integrate LLM APIs (OpenAI, Anthropic, Azure OpenAI) with auth, rate-limiting, and cost controls.
• Instrument applications for evaluation — output logging, quality scoring, human-feedback loops.
• Optimize latency and token cost through caching, batching, and model routing.
• Translate client requirements into GenAI feature specs.
• Communicate tradeoffs (cost, latency, accuracy) to non-technical stakeholders.
• Collaborate with the Architect and Data Scientists on shared components.
• Document architecture and prompt design decisions for handoff and maintainability.

Skills

Strong Python

GenAI/LLM app development

LangChain

LlamaIndex

Vector databases

Neo4j knowledge graph

LoRA/QLoRA fine-tuning

API design

LLMOps & prompt-versioning

Client-facing collaboration

Tools

LangChain

LlamaIndex

Pinecone

Weaviate

pgvector

Neo4j

LangSmith

Weights & Biases

Azure AI Foundry

AWS Bedrock

Google Vertex AI

Job description

Builds generative AI applications — LLM-powered features, RAG pipelines, and enterprise search that ship to production, not just a demo.

KEY RESPONSIBILITIES

• Build GenAI applications — LLM-powered features, copilot/chat experiences, enterprise search
• Design and implement RAG pipelines: chunking strategy, embedding selection, hybrid retrieval, re-ranking, GraphRAG where structured retrieval is needed
• Fine-tune and adapt models (LoRA/QLoRA) when prompt engineering and RAG aren't sufficient
• Engineer and version production prompts; build prompt/context management into the application layer
• Integrate LLM APIs (OpenAI, Anthropic, Azure OpenAI) and open-source model endpoints with auth, rate-limiting, and cost controls
• Instrument applications for evaluation — output logging, quality scoring, human-feedback loops
• Optimize latency and token cost through caching, batching, and model routing strategies
• Translate client business requirements into concrete GenAI feature specifications
• Communicate technical tradeoffs (cost, latency, accuracy) to non-technical product stakeholders
• Collaborate with the Agentic AI Architect and Data Scientists on shared components
• Document architecture and prompt design decisions for handoff and maintainability

REQUIREMENTS & SKILLS

• 4–8 yrs software engineering, with 1–3 yrs hands-on GenAI/LLM application building
• Strong Python; experience with LangChain, LlamaIndex, or equivalent orchestration frameworks
• Vector databases and embedding strategies (Pinecone, Weaviate, pgvector), plus knowledge-graph/graph-database tooling (Neo4j) where relevant
• Understands LLM failure modes (hallucination, context-window limits, cost blowup) and designs mitigations
• Experience with model fine-tuning techniques (LoRA/QLoRA) and evaluation harnesses
• Hands-on with enterprise GenAI/agentic platforms — Microsoft Azure AI Foundry, AWS Bedrock (incl. Strands Agents SDK), and Google Vertex AI; open-source frameworks (LangChain, LlamaIndex) a good-to-have where no platform is mandated
• API design and integration experience, including auth, rate limiting, and streaming responses
• Familiarity with prompt-versioning and LLMOps tooling (LangSmith, Weights & Biases, or similar)
• Clear technical writing — documents a RAG architecture for a non-technical stakeholder
• Comfortable working directly with client engineers during embedded delivery
• Collaborative — works with architects, data scientists, and QA without needing everything pre-specified