MLOps Engineer - Pune

Tsys Global Solutions is seeking a skilled MLOps Engineer to join their team in Pune. This role focuses on designing, implementing, and managing robust CI/CD pipelines for AI and ML model development, deployment, and operationalization, including advanced RAG systems and LLMs.

Key Responsibilities:

• Design and implement CI/CD pipelines for AI and ML model training, evaluation, and RAG system deployment, encompassing LLMs, vector databases, embedding and reranking models, governance, observability systems, and guardrails.
• Provision and manage AI infrastructure across cloud hyperscalers (AWS/GCP), utilizing infrastructure-as-code tools, with a strong preference for Terraform.
• Maintain containerized environments (Docker, Kubernetes) optimized for GPU workloads and distributed compute.
• Support deployments of vector databases, feature stores, and embedding stores (e.g., pgVector, Pinecone, Redis, Featureform, MongoDB Atlas).
• Monitor and optimize the performance, availability, and cost of AI workloads using observability tools (e.g., Prometheus, Grafana, Datadog, or managed cloud offerings).
• Collaborate effectively with data scientists, AI/ML engineers, and platform team members to ensure seamless transitions from experimentation to production.
• Implement and enforce security best practices, including secrets management, model access control, data encryption, and audit logging for AI pipelines.
• Assist in the deployment and orchestration of agentic AI systems (LangChain, LangGraph, CrewAI, Copilot Studio, AgentSpace, etc.).

Must-Have Qualifications:

• A minimum of 4 years of experience in DevOps, MLOps, or infrastructure engineering, with at least 2 years specifically in AI/ML environments.
• Hands-on experience with cloud-native services (AWS Bedrock/SageMaker, GCP Vertex AI, or Azure ML) and managing GPU infrastructure.
• Strong proficiency with CI/CD tools (GitHub Actions, ArgoCD, Jenkins) and configuration management tools (Ansible, Helm).
• Proficiency in scripting languages such as Python and Bash; knowledge of Go or similar is a plus.
• Experience with monitoring, logging, and alerting systems tailored for AI/ML workloads.
• A deep understanding of Kubernetes and container lifecycle management.

Bonus Attributes:

• Exposure to MLOps tooling like MLflow, Kubeflow, SageMaker Pipelines, or Vertex Pipelines.
• Familiarity with prompt engineering, model fine-tuning, and inference serving.
• Experience with secure AI deployment and adherence to compliance frameworks.
• Knowledge of model versioning, drift detection, and scalable rollback strategies.

Abilities and Soft Skills:

• Demonstrated ability to work with a high degree of initiative, accuracy, and attention to detail.
• Capability to prioritize multiple assignments effectively and meet established deadlines.
• Ability to interact professionally and efficiently with staff and customers.
• Excellent organizational skills.
• Strong critical thinking abilities for moderately to highly complex problems.
• Flexibility to adapt to evolving business needs.
• Capacity to work creatively and independently with minimal supervision.
• Ability to leverage experience and judgment to achieve assigned goals.
• Experience navigating complex organizational structures.