Strong proficiency in Python (async, typing, packaging, unit testing); solid grounding in data structures and algorithms

- Hands‑on with AWS —specifically Amazon Bedrock , SageMaker , S3 , Lambda , Step Functions , CloudWatch , IAM ; vector search - pgvector , OpenSearch vector
- Working knowledge of LangChain/LangGraph , RAG architectures, prompt design & evaluation; familiarity with guardrails (policy, PII/Secrets redaction), HITL patterns
- Experience with ETL/data processing (Pandas / Spark) and NoSQL/SQL stores (DynamoDB/Postgres)
- Proficiency with Git , CI/CD (GitHub Actions/Jenkins), Docker , and basic Kubernetes concepts
- Understanding of microservices and event‑driven integrations (queues, webhooks); observability (logging, metrics, tracing)
- Bonus :ReactJS for full‑stack prototypes; Copilot usage patterns for engineering productivity; document pipelines
- What you must bring (Experience)
- 4-7 years of software engineering experience GenAi services focused, including 2+ years building or integrating GenAI/ML solutions
- Bachelor’s degree in Engineering/Computer Science (or equivalent)
- Practical understanding of IT + business concepts and how AI features translate to measurable outcomes
- Responsibilities - What will you do
- Design and develop AI‑powered backend systems and services that leverage Bedrock (model selection, orchestration), RAG pipelines (indexing, retrieval, evaluation),



and S3 content sources
- Build secure APIs that integrate AI features into internal apps; partner with UI teams on full‑stack delivery
- Implement prompt engineering , evaluation harnesses, safety filters, and HITL review workflows; measure quality (precision, hallucination rate, latency, cost)
- Create ETL/ingestion jobs and embeddings pipelines; optimize chunking, metadata, and retrieval performance
- Productionize with CI/CD, IaC, logging/monitoring , and cost/latency optimization; contribute to runbooks and SLOs
- Collaborate with product owners, domain SMEs, data engineering, security, and compliance to align on value, controls, and go‑live readiness
- Champion reusability by packaging patterns (RAG, agents, evaluation) as internal accelerators and documenting best practices
- Nice‑to‑have / Good‑to‑have
- Experience with agentic workflows (task decomposition, tools, function‑calling) and evaluation frameworks
- Knowledge of SageMaker model endpoints, fine‑tuning/parameter‑effective tuning, and feature stores
- Familiarity with pgvector/OpenSearch , prompt/test case libraries , cost observability , and Copilot productivity dashboards

Pay: ₹1,600,000.00 - ₹2,000,000.00 per year

Benefits :

- Health insurance
- Provident Fund

Application Question(s):

- current ctc & notice period?

Work Location: In person

📌 Gen Ai Engineer (Gurugram)
🏢 Dinero Solutions
📍 Gurugram