Forward Deployed Engineer - GenAI

Systems Limited; Karachi Division; On-site; PKR 2,000,000 - 3,200,000; Full time

Job summary

Systems Limited is seeking a seasoned GenAI software engineer to build LLM-powered features, RAG pipelines, and enterprise search integrations. You will design and fine-tune models, implement prompt management, and integrate various AI APIs to deliver production-grade GenAI solutions for client projects.

You will work with architects, data scientists, and QA to translate client requirements into concrete features, optimize latency and costs, and document architecture decisions to ensure

Qualifications

• 4–8 yrs software engineering with 1–3 yrs GenAI/LLM app building.
• Strong Python; LangChain, LlamaIndex or equivalent orchestration frameworks.
• Experience with vector databases and embedding strategies; graph tooling where relevant.
• Understand LLM failure modes and design mitigations.
• Model fine-tuning (LoRA/QLoRA) and evaluation harnesses.
• Hands-on with enterprise GenAI platforms and open-source frameworks.
• API design and integration including auth, rate limiting, and streaming.
• Prompt-versioning and LLMOps tooling; clear technical writing.
• Comfortable working with client engineers during embedded delivery.
• Collaborative across architects, data scientists, and QA.

Responsibilities

• Build GenAI applications — LLM-powered features, copilot/chat experiences, enterprise search
• Design and implement RAG pipelines: chunking strategy, embedding selection, hybrid retrieval
• 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 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 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

Skills

Problem solving

Team collaboration

Communication

Tools

Python

LangChain

LlamaIndex

Neo4j

Pinecone

Weaviate

pgvector

Azure AI Foundry

AWS Bedrock

Vertex AI

LangSmith

Weights & Biases

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