Position / Designation : Sr. MLOps & AgentOps Engineer

Qualification : Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field.

Years of Experience : Minimum 5 - 7+ years in DevOps, platform engineering, MLOps, or a closely related role. Experience running production AI, ML, or agentic workloads is strongly preferred. Exposure to high-availability, regulated, or enterprise-scale environments is an advantage.

Permanent / Contract : Fulltime

Hybrid / Remote : Hybrid weekly 3 days from office

Job Role & Responsibility :

- Design and implement CI/CD pipelines for models, prompts, agents, and supporting infrastructure across development, test, and production environments.

- Build and maintain deployment automation for AI workloads, including versioning and rollback mechanisms, environment promotion workflows, and runtime safeguards.

- Set up and operate observability for AI applications and agents : tracing, monitoring, alerting, token-consumption analysis, latency tracking, and incident diagnostics.

- Implement evaluation pipelines and acceptance gates for quality, groundedness, task adherence, safety, and agent-specific behaviour.

- Drive prompt lifecycle management, RAG optimisation, and semantic retrieval tuning, and integrate vector- or search-based knowledge components where needed.

- Partner with security, engineering, and data teams to build identity, secrets management, compliance controls, and cost optimisation into the operating model.

- Contribute to platform automation, runbooks, and on-call readiness, and lead post-incident reviews that improve production AI services.

Skills :

Technical Skills :

- Cloud & IaC : Azure, Terraform or Bicep, infrastructure-as-code patterns.

- CI/CD & containers : GitHub Actions or Azure DevOps Pipelines, Docker, Kubernetes (AKS).

- AI platform : Microsoft Foundry (deployments, evaluations, tracing), Azure AI Search, vector stores.

- Observability : Azure Monitor, Application Insights, Log Analytics, OpenTelemetry.

- Programming : Python for automation, tooling, evaluation orchestration, and operational support.

2. Desired Attributes :

- Strong analytical and problem-solving abilities with meticulous attention to detail.

- Effective communicator able to interface with engineering, data, security, and operations stakeholders.

- Self-motivated professional comfortable delivering in a hybrid model, balancing on-site client engagement with remote work.

- Collaborative team player committed to continuous improvement.

3. Certifications :

- Microsoft Certified : DevOps Engineer Expert (AZ-400) or Azure Administrator Associate (AZ-104) preferred.

- Microsoft Certified : Azure AI Engineer Associate (AI-102) or successor, or Fabric Data Engineer Associate (DP-700), a plus.

4. Qualifications :

- DevOps and platform engineering : Experience operating production cloud workloads with CI/CD, monitoring, and infrastructure automation.

- MLOps / LLMOps / AgentOps : Hands-on practice in deployment, monitoring, retraining or re-evaluation, and controlled release management for AI systems.

- Observability : Strong grasp of logs, metrics, traces, runtime telemetry, and production diagnostics for AI workloads.

- Generative AI engineering : Familiarity with retrieval-augmented systems, prompt engineering, tool-calling flows, and agent behaviour debugging.

- Operational excellence : A reliability-first mindset with strong attention to security, incident response, and cost-performance trade-offs.

Joining Date : Immediate