AI Automation SME (Python Developer)
Tata Consultancy Services
2 - 5 years
Hyderabad
Posted: 17/02/2026
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Job Description
Responsibilities
- Solutioning & Design: Translate business requirements into automation architectures: Python microservices, event-driven jobs, orchestrations (Airflow/Prefect), and serverless patterns.
- Build & Integrate: Develop robust Python code (FastAPI/Flask/Django), reusable libraries, and REST/GraphQL APIs; integrate with data platforms, queues, and external systems.
- AI Enablement: Implement ML/LLM-powered automations (inference APIs, prompt/response pipelines, retrieval augmentation), balancing performance, cost, and controls.
- Data Engineering: Create reliable ETL/ELT pipelines (pandas/pySpark), data quality checks, and schema/version management.
- DevOps & SRE: Own CI/CD (GitHub Actions/Azure DevOps/Jenkins), containerization (Docker), orchestration (Kubernetes), IaC (Terraform), secrets management, and runtime observability.
- Quality & Security: Establish unit/integration/contract tests (pytest), code reviews, static analysis, vulnerability scanning, and compliance guardrails.
- Agile Ways of Working: Operate within Scrum/SAFe; drive backlog refinement, technical spikes, and definition of done; coach dev squads on automation best practices.
- Stakeholder Management: Collaborate with Product Owners, SMEs, and platform teams; articulate trade-offs, estimates, and release plans; produce clear documentation/runbooks.
- Mentorship: Upskill engineers on Python, automation patterns, and AI safety; lead brown-bags and code clinics.
Required Qualifications
- Experience: 5+ years in software engineering with 4+ years building production automation in Python.
- Core Python: Advanced knowledge of concurrency (asyncio, multi-processing), packaging, type hints, error handling, profiling, and performance tuning.
- Frameworks: FastAPI/Flask/Django; job orchestration (Airflow/Prefect); message brokers (Kafka/RabbitMQ); task queues (Celery/RQ).
- Testing & Quality: pytest, coverage, contract tests, mocks, property-based testing; API versioning and backward compatibility.
- Cloud & Runtime: Azure/AWS/GCP, Docker/Kubernetes, Secrets/KeyVault, monitoring (Prometheus/Grafana), tracing (OpenTelemetry).
- Data & Integration: SQL/NoSQL, dataframes (pandas), file/object stores (S3/Blob), schema management, streaming and batch pipelines.
- DevOps: Git, branching strategies, CI/CD, artifact management, IaC (Terraform/Bicep), security scanning (SAST/DAST).
- Soft Skills: Strong communication, stakeholder influence, and the ability to lead technical delivery in agile environments.
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