Forward Deployed Engineer - GenAI

Systems Limited; Malaysia; On-site; MYR 120,000 - 180,000; Full time

Job summary

Systems Limited in Malaysia is seeking a skilled GenAI/LLM engineer to build production-ready GenAI applications, focusing on LLM-powered features, RAG pipelines, and enterprise search.

You will design RAG architectures, fine-tune models (LoRA/QLoRA), integrate API endpoints, and collaborate with data scientists to deliver scalable solutions; strong Python is required.

Qualifications

• 4–8 years software engineering with 1–3 years hands-on GenAI/LLM app building.
• Proficient in Python; familiar with LangChain or LlamaIndex.
• Experience with vector databases and embedding strategies.
• Knowledge of LLM failure modes and mitigations.
• Experience with model fine-tuning techniques (LoRA/QLoRA).
• Hands-on with enterprise GenAI platforms such as Azure AI Foundry, AWS Bedrock, and Vertex AI.
• Strong API design and integration skills.
• Good technical writing to document RAG architecture for stakeholders.

Responsibilities

• Build GenAI applications with LLM-powered features and enterprise search.
• Design and implement RAG pipelines: chunking, embeddings, hybrid retrieval, re-ranking.
• Fine-tune and adapt models (LoRA/QLoRA) when prompts are insufficient.
• Engineer and version production prompts; embed prompt management.
• Integrate LLM APIs with auth, rate limits, and cost controls.
• Instrument applications for evaluation: logging, quality scoring, human feedback loops.
• Optimize latency and token cost via caching, batching, and routing strategies.
• Translate client requirements into GenAI feature specs.
• Communicate tradeoffs to non-technical stakeholders.
• Collaborate with Architect and Data Scientists on shared components.
• Document architecture and prompt design decisions for handoff.

Skills

Python programming

LangChain/LlamaIndex

Vector databases

LLM/GenAI

API design

LLMOps tooling

Technical writing

Collaborative

Tools

Pinecone

Weaviate

Neo4j

Azure AI Foundry

Vertex AI

LangChain

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