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Tech Stack
Tools & technologiesAWSAzureCloudDockerETLFlaskGoogle Cloud PlatformKubernetesMicroservicesPySparkPythonSQL
About the role
Key responsibilities & impact- Define and lead end-to-end architecture for enterprise GenAI platforms and use cases
- Design scalable agentic systems (single-agent, multi-agent, orchestration frameworks)
- Establish reference architectures, design patterns, and reusable frameworks
- Lead architecture decisions on RAG vs fine-tuning vs hybrid approaches
- Conduct technology evaluations (LLMs, vector DBs, orchestration frameworks) and recommend best-fit solutions
- Design and implement complex agentic workflows with tool calling, function orchestration, and memory strategies
- Build enterprise-grade RAG pipelines with strong focus on retrieval accuracy and evaluation
- Drive prompt architecture standards (prompt libraries, chaining, orchestration governance)
- Optimise solutions for latency, cost, scalability, and reliability
- Lead development of GenAI platforms, APIs, and microservices (FastAPI, Flask, etc.)
- Define engineering best practices: coding standards, testing, packaging, observability
- Ensure seamless integration with enterprise data platforms, APIs, and business applications
- Collaborate with MLOps teams for CI/CD, deployment pipelines, versioning, and monitoring
- Define and enforce LLM guardrails (hallucination control, safety filters, policy enforcement)
- Implement evaluation frameworks (RAG evaluation, prompt testing, benchmarking)
- Ensure compliance with data security, privacy, and enterprise governance standards
- Drive adoption of Responsible AI practices (bias mitigation, explainability, auditability)
- Partner with Data Engineering teams on Data ingestion, pipelines, and quality controls
Requirements
What you’ll need- 12–15 years total experience, with 3+ years in GenAI / LLM-based systems
- Proven experience in leading architecture and delivery of enterprise solutions
- Strong hands-on experience with LLMs (Claude, OpenAI, etc.)
- RAG pipelines and retrieval optimisation
- GPT + Agentic AI implementation experience
- Experience with LangChain, LangGraph, or similar frameworks
- Agent orchestration and tool-calling architectures
- Strong Python/Pyspark engineering expertise (production-grade development) with proven API integration experience
- Deep data analysis experience and handling large volume of data
- Fabric/Azure Databricks/Snowflake data engineering integration skills
- Good exposure to Cloud platforms (Azure/AWS/GCP)
- SQL
- Containers, CI/CD, monitoring
- Hands-on experience with Azure / AWS / GCP
- Familiarity with Containers (Docker/Kubernetes)
- CI/CD pipelines
- Monitoring & observability
- Prior experience in Data Engineering (ETL/ELT, pipelines, orchestration) or Data Science / ML lifecycle (especially NLP) or Analytics engineering / data products
Benefits
Comp & perks- Health insurance
- Retirement plans
- Paid time off
- Flexible work arrangements
- Professional development
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
GenAI PlatformsLLMsRAG PipelinesPythonPysparkSQLData EngineeringNLPETL/ELTData Analysis