Forward Deployed Engineer - GenAI

iSystems Ltd; Lahore; On-site; PKR 200,000 - 420,000; Full time

Job summary

iSystems Ltd in Lahore, Pakistan, is seeking a Software Engineer with 4–8 years of experience and hands-on GenAI/LLM application building. You will design RAG pipelines, fine-tune models, integrate OpenAI/Azure and open-source endpoints, and optimize latency and costs while communicating complex tradeoffs to clients.

You'll collaborate with architects, data scientists, and QA to translate client requirements into concrete GenAI features, document architecture, and maintainable prompts, ensuring

Qualifications

• 4–8 yrs software engineering with 1–3 yrs GenAI/LLM app building.
• Strong Python skills and hands-on GenAI experience.
• Familiar with LangChain, LlamaIndex, vector stores, and graphs.

Responsibilities

• Build GenAI applications — LLM-powered features and enterprise search.
• Design and implement RAG pipelines with embedding selection and retrieval.
• Fine-tune models (LoRA/QLoRA) and manage prompts in production.

Skills

GenAI application development

Python programming

LangChain/LlamaIndex

Vector databases

Embeddings & retrieval

Model fine-tuning (LoRA/QLoRA)

Tools

Pinecone

Weaviate

Neo4j

LangChain

LlamaIndex

Azure OpenAI

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