Job highlights

- 10+ years experience in AI multi-agent systems, AWS cloud platforms, Terraform, Python, and model fine-tuning techniques
- Design and deploy AI agent workflows, build durable AI pipelines using Apache Airflow and AWS Step Functions, implement observability with Datadog and Grafana, and manage data platforms and streaming

Forward Deployed Software Engineer

Experience : 10+ yrs

Location : Bangalore / Hyderabad

AI & Agent SystemsAgent Design & Workflows:

Multi-agent systems, tool-using/self-learning agents, Model Context Protocol (MCP) for tool publishing, retrieval-augmented generation (RAG), Graph-RAG,

prompt engineering, model selection, and accuracy-per-cost routing.Evaluation & Guardrails: LLM evaluation harnesses (labeled sets, LLM-as-judge, RAGAS), guardrails, prompt-injection defense, audit trails, and rollback mechanisms.

Orchestration & AutomationWorkflow

Orchestration: Apache Airflow, AWS Step Functions, Amazon EventBridge for building durable, retryable, and idempotent AI pipelines.Task Management: Idempotent task design, retries, failure recovery, and backfills.

AI-Assisted Development

AI Coding Assistants: Claude, Cursor, Codex-style tools for scaffolding, refactoring, test generation, and code review

Cloud & DeploymentCloud Platforms:

AWS (Bedrock, S3, Glue, Lambda, EMR, Athena, CloudFormation, EventBridge), Kubernetes, Docker.Infrastructure as Code: Terraform (reusable modules), GitHub Actions, Jenkins, CI/CD, GitOps-style declarative deployments.

Observability & LLMOps

- Monitoring & Tracing: Datadog, Grafana,



Amazon CloudWatch, LangSmith for distributed tracing, prompt versioning, token/cost and latency monitoring, alerting, and regression-eval gating.

Data Platforms & Streaming

- Databases: Snowflake, PostgreSQL/pgvector, MongoDB, Amazon Redshift, Delta Lake, Apache Iceberg, Oracle, SQL Server, Teradata.
- Event Streaming: Apache Kafka, Amazon Kinesis, Apache Flink.
- Vector Search: Pinecone, embeddings, chunking, reranking.

Programming & Model Training

- Languages & Frameworks: Python (pandas, PySpark, REST API), SQL, PL/SQL, Unix/Linux shell scripting, pytest.
- Model Training/Serving: Fine-tuning (LoRA/PEFT, SFT, DPO/RLHF), distillation, quantization (GGUF/AWQ/GPTQ), self-hosted inference (vLLM, TGI), Hugging Face Transformers, PyTorch

Governance, Risk & Compliance

- Controls & Validation: Regulatory reporting datasets, audit trails, RBAC, PII masking, data quality controls (Excellent Expectations, dbt tests), governed schemas/catalogs (AWS Glue Catalog, Erwin), dimensional/semantic data modeling.

Project Experience

- End-to-End AI System: Built and deployed a custom agentic GenAI system (AROS) using AWS Bedrock, Airflow, Pinecone, and Kubernetes, with CI/CD via GitHub Actions and Terraform, monitored by Datadog and Grafana, and governed by audit trails and RBAC.
- Data Platform Modernization: Consolidated multiple databases (Redshift, Oracle, Teradata, SQL Server) into a single governed source, orchestrated with Airflow and validated with Great Expectations

📌 Forward Deployed Software Engineer (Hyderabad)
🏢 Mphasis
📍 Hyderabad