Sr Analyst, DevOps

• Deployed, tested and enterprise-integrated AI services, APIs and solutions validated for reliability, security and performance.
• Validated, documented data pipelines supporting AI training, inference and production workflows.
• Connectors, API contracts and integration layers linking AI capabilities to business applications.
• Reusable service templates and implementation patterns adopted across AI projects
• CI/CD workflows for AI models and service deployments across all environments.
• Monitoring and observability setup through dashboards, alerts and logging for AI workloads closely monitoring performance, costs and reliability.
• Hardened AWS and Azure infrastructure and security configurations for AI workloads.
• Evaluation reports benchmarking AI models, APIs and frameworks for accuracy, latency, costs and scalability.
• Architecture-aligned technical documentation, including API references, deployment runbooks and known limitations
• Operational runbooks for incident response, troubleshooting and maintenance of AI workloads.

• 3-5+ years in software engineering, data engineering or ML engineering roles
• Hands-on experience building and deploying AI/ML solutions in production environments (not just POCs/notebooks)
• Experience translating prototypes or proof-of-concepts into scalable production-grade AI based solutions
• Working experience with AWS and/or Azure, particularly AI/ML services (e.g., SageMaker, Bedrock, Azure ML, Azure Foundry)
• Experience with IAM, networking & security configuration for cloud workloads.
• Experience with containerization and orchestration (Docker, Kubernetes/ECS/EKS)
• Practical experience with LLMs, prompt engineering and agent orchestration frameworks (e.g. Langchain/LangGraph, MS Agent Framework/Semantic Kernel)
• Experience implementing RAG (retrieval-augmented generation) architectures, including vector databases (Azure AI Search, AWS OpenSearch, Bedrock KB, AWS DocumentDB)
• Familiarity with model serving and inference patterns, including real-time, batch and API based approaches.
• Strong API development experience (REST, GraphQL)
• Proficiency in Python with CI/CD pipeline experience (GitHub Actions, Azure DevOps)

• Bachelor’s degree in computer science, Software Engineering, Data Science or related field
• Proficiency in Python, with familiarity in JavaScript/TypeScript
• Skilled in LLMs, prompt engineering, RAG architectures, vector databases and agent orchestration frameworks.
• Proficient in REST/GraphQL API build, microservices and integration patterns
• Proficient in building ETL/ELT pipelines and validating data using cloud native tools like AWS Glue or Azure Data Factory
• Proficient in CI/CD pipelines, containerization (Docker, Kubernetes/ECS/EKS), and MLOps practices including model deployment, versioning, and monitoring
• Strong problem solving and analytical thinking with a required ability to translate ambiguous requirements into working solutions
• Effective technical communicator with a required ability to clearly communicate solutions to both technical and non-technical stakeholders
• Demonstrated decisiveness taking ownership of technical decisions and solutions willing to make calculated judgement calls under ambiguity rather than defaulting to indecision.
• Adaptable to work with evolving AI tools/frameworks in a fast-changing landscape

• AWS Certified Machine Learning Engineer - Associate
• AWS Certified Generative AI Developer - Professional

• Azure AI App and Agent Developer Associate

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