Forward Deployed Engineer - GenAI

Systems Limited; Islamabad; On-site; PKR 1,500,000 - 2,400,000; Full time

Job summary

Systems Limited is seeking a skilled software engineer to build GenAI applications, including LLM-powered features, RAG pipelines, and enterprise search. You will design and implement end-to-end solutions, optimize latency, and collaborate with architects and data scientists to deliver production-ready capabilities.

The role requires strong Python, experience with LangChain and vector databases, and familiarity with model fine-tuning (LoRA/QLoRA).

Qualifications

• 4–8 yrs software engineering, with 1–3 yrs GenAI/LLM app building.
• Strong Python; experience with LangChain, LlamaIndex, or equivalent.
• Vector databases and embedding strategies; graph tooling experience helpful.
• Understands LLM failure modes and mitigations.
• Model fine-tuning techniques (LoRA/QLoRA) and evaluation harnesses.
• Experience with enterprise GenAI platforms and API design; LLMOps tooling experience.

Responsibilities

• Build GenAI applications - LLM-powered features and RAG pipelines.
• Design and implement RAG pipelines; embedding selection and retrieval.
• Fine-tune and adapt models (LoRA/QLoRA) when needed.
• Engineer and version production prompts; document architecture decisions.
• Integrate LLM APIs and open-source endpoints with auth and rate limits.
• Instrument apps for evaluation: logging, quality scoring, feedback.
• Optimize latency and token cost via caching and model routing.
• Communicate tradeoffs to non-technical stakeholders.
• Collaborate with architects, data scientists, and QA; embed delivery with clients.

Skills

Python

LangChain

LlamaIndex

Vector databases

Pinecone

Weaviate

Neo4j

LLM failure modes

LoRA/QLoRA

LLMOps

API design

Prompt-versioning

Tools

LangChain

LlamaIndex

OpenAI API

Azure OpenAI

Weaviate

Pinecone

Neo4j

Job description

ABOUT:

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