Forward Deployed Engineer

Key Responsibilities:-

• Build and maintain ingestion pipelines from BT's source systems (docs, wikis, ticketing, databases)
• Own ETL/ELT jobs, chunking strategy, embedding generation, and pipeline monitoring
• Implement data quality checks, deduplication, and access-control tagging on ingested content
• Manage schema and version changes as BT's underlying systems evolve
• Implement RAG pipelines, multi-agent orchestration, and prompt/context construction logic
• Tune retrieval quality (hybrid search, reranking, context window management)
• Integrate with the client's chosen LLM providers and manage prompt versioning
• Own latency and cost optimization for inference calls
• Own deployment (cloud/on-prem per BT's constraints), CI/CD, and environment management
• Handle auth/SSO, network policies, and compliance requirements specific to BT (telecom-grade security)
• Set up observabilitylogging, tracing, alertingfor the whole pipeline
• Manage integration with BT's existing enterprise systems (ticketing, CRM, internal tools)
• Build evaluation harnesses (accuracy, faithfulness, retrieval precision/recall) for the context pipeline
• Run regression testing whenever prompts, models, or data sources change
• Own guardrailsPII detection, hallucination checks, content filtering
• Publish quality dashboards/reports used in BT governance reviews

Required Skills:-

• Python and SQL
• ETL/ELT tooling (Airflow, dbt, or equivalent) and document chunking/embedding pipelines
• Vector databases (Pinecone, Weaviate, pgvector, or similar) and embedding models
• RAG architectures, hybrid search, reranking, and context window management
• Multi-agent orchestration frameworks (LangChain, LangGraph, or similar)
• Prompt engineering and prompt versioning; working knowledge of major LLM APIs (OpenAI, Anthropic, Azure OpenAI, Bedrock)
• Cloud platforms (AWS/Azure/GCP) and on-prem deployment patterns; Docker/Kubernetes
• CI/CD pipelines and environment management
• SSO/OAuth, enterprise auth, and networking/security fundamentals for regulated environments
• Observability tooling (Prometheus, Grafana, OpenTelemetry) and building LLM evaluation harnesses
• Regression testing practices and guardrail/PII-detection tooling
• 4+ years in data/ML engineering or applied AI, ideally with a prior LLM system in production

Nice to Have: Experience in telecom or another regulated industry; knowledge graphs/ontology-based retrieval; LLM cost/latency optimization (caching, batching, model routing); exposure to enterprise ticketing/CRM platforms (ServiceNow, Salesforce, Jira); experience presenting quality metrics to governance stakeholders.

Interested Candidates Kindly share your updated resume directly to @

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