Senior GenAI Software Engineer
Tasks
- Apply LLMOps and MLOps evaluation and monitoring
- Build agent orchestration and tool calling
- Build backend services and microservices integrating LLMs
- Build containerized services
- Collaborate with architecture data security and business
- Define API quality and security checks
- Deploy CI CD pipelines and manage releases
- Design and develop generative AI applications and services
- Document architecture decisions and reusable playbooks
- Implement LLM guardrails and PII protection
- Implement observability with logging, metrics, tracing
- Implement prompt and artifact versioning
- Implement rag pipelines
- Implement security testing and code quality gates
- Support code reviews and mentor junior engineers
- Write unit, integration, and contract tests
Perks/Benefits
- Career growth
- Certification support
- Continuous learning
- Flexible work-life balance
- Hybrid work model
- Inclusive collaborative environment
- Onsite and online training
- Professional mentoring
Skills/Tech-stack
AI Studio | API rate-limiting | AWS | Amazon Bedrock | Authentication | Azure | Azure AI | Azure AI Studio | Azure DevOps | Azure OpenAI | BDD | CI/CD | Chaos Engineering | Chaos Testing | Contract testing | DAST | Dependency Scanning | Docker | Embeddings | Experiment tracking | Express | Faiss | FastAPI | Flask | Function Calling | GCP | Git | GitHub Actions | GitLab CI | GraphQL | Infra-as-code | Integration Testing | Jenkins | Kafka | Kubernetes | LLMOps | Langchain | Linting | Llamaindex | Logging | MLOps | MLflow | Metrics | OAuth 2.0 | OAuth2 | OIDC | Observability | OpenAI | OpenTelemetry | PGVector | Pinecone | Prompt Flow | Pub/Sub | Python | RAG | REST | Rate Limiting | SAST | SLO/SLA | Semantic Kernel | Sustainability Governance | TDD | Terraform | Tool-Calling | Tracing | Unit Testing | Vector Databases | Vertex AI | Weights and Biases | “as-code”
Education
N/A
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