Forward Deployed Engineer - GenAI

iSystems Ltd; Karachi Division; In loco; PKR 2.000.000 - 4.000.000; Tempo pieno

Descrizione del lavoro

iSystems Ltd builds generative AI applications — LLM-powered features, RAG pipelines, and enterprise search that ship to production, not just a demo. You will design and implement robust GenAI systems, fine-tune models, and integrate multiple cloud and open-source platforms.

You will work withArchitects and Data Scientists to translate client requirements into concrete feature specs, while documenting decisions for maintainability and handoff.

Competenze

• 4–8 yrs software engineering, with 1–3 yrs GenAI/LLM app building.
• Strong Python; experience with LangChain, LlamaIndex, or equivalent.
• Vector DBs and embedding strategies (Pinecone, Weaviate, pgvector).
• Understands LLM failure modes and mitigations.
• Model fine-tuning (LoRA/QLoRA) and evaluation harnesses.
• Experience with enterprise GenAI platforms (Azure AI Foundry, AWS Bedrock, Vertex AI) and open-source frameworks.
• API design and integration with auth, rate limiting, streaming.
• Prompt-versioning and LLMOps tooling (LangSmith, Weights & Biases).
• Clear architecture docs and ability to work with client engineers.

Mansioni

• Build GenAI applications — LLM-powered features, copilot/chat experiences.
• Design and implement RAG pipelines: chunking, embedding, hybrid retrieval, re-ranking.
• Fine-tune and adapt models (LoRA/QLoRA) when needed.
• Engineer and version production prompts; integrate prompt management.
• Integrate LLM APIs and open-source model endpoints with auth and cost controls.
• Instrument apps for evaluation — output logging, quality scoring, human feedback loops.
• Optimize latency and token cost via caching, batching, and model routing.
• Translate client requirements into concrete GenAI feature specs.
• Document architecture and prompt design decisions for handoff and maintainability.
• Collaborate with architects and data scientists during embedded delivery.

Conoscenze

Python programming

GenAI / LLM development

Collaborative work

Strumenti

Python

LangChain

LlamaIndex

Pinecone

Weaviate

pgvector

Neo4j

Azure AI Foundry

AWS Bedrock

Vertex AI

LangSmith

Weights & Biases

Descrizione del lavoro

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