Senior ML Ops, LLM Ops Engineer – Analytics as Service

This is a fully remote position, open to applicants in India.

📋 Description

• Oversee and enhance managed services processes and tools to improve client operations.

• Direct delivery teams in executing service management strategies and initiatives for performance enhancement.

• Apply project management expertise to optimize operations and decrease client expenses.

• Create and implement automation frameworks to elevate service delivery and operational efficiency.

• Collaborate with stakeholders to pinpoint opportunities for business process improvement and transformation.

• Evaluate complex data to provide insights and recommendations for ongoing process enhancement.

• Maintain professional and technical standards during client engagements.

• Work closely with clients to comprehend their needs and deliver customized managed services solutions.

• Guide junior team members and engineers through mentorship.

• Take ownership of deployment architecture for AI solutions on AWS.

• Design and manage CI/CD, Infrastructure as Code, and release standards across projects.

• Spearhead the integration of AI solutions into legacy and regulated environments, ensuring compliance with identity, security, and governance standards.

• Establish scalable models and agent serving with vector and retrieval infrastructure.

• Implement observability, evaluation, and cost control measures for production AI workloads.

• Define security, governance, and responsible-AI strategies for deployments.

• Develop reusable deployment accelerators and mentor engineers.

• Share field insights and identify product gaps with the broader practice.

• Act as the technical lead for deployment while integrated with an enterprise customer’s team.

⛳️ Requirements

• Extensive DevOps or platform engineering experience with a focus on production deployments.

• Profound knowledge of CI/CD, Docker, and Kubernetes.

• Strong expertise in Terraform / Infrastructure as Code.

• Excellent understanding of AWS services relevant to deployment.

• Solid background in identity, security, networking, and enterprise integration.

• Proven automation skills and experience in codifying build and run processes.

• Extensive hands-on experience in deploying LLM and agentic applications to production (LLMOps).

• Familiarity with serving, scaling, retrieval infrastructure, observability, evaluation, and responsible AI.

• Preferred: Experience with enterprise AI platforms such as Palantir Foundry, Databricks, and Snowflake.

• Preferred: Familiarity with MLOps tooling at scale.

• Experience in regulated industries is a plus.

• Preferred: SRE or reliability experience.

• Previous consulting, customer success, or forward-deployed experience is desirable.

• AWS Certified DevOps Engineer – Professional and/or AWS Certified Solutions Architect – Professional; CKA or a cloud AI/ML certification is preferred.

• Technical skills in: AWS Bedrock, SageMaker, Lambda, ECS, EKS, Step Functions, S3, API Gateway, IAM, CloudWatch.

• Technical skills in: Docker, Kubernetes, Helm, Terraform, Ansible.

• Technical skills in: GitHub Actions, GitLab CI, Jenkins, ArgoCD.

• Technical skills in: model and agent serving and scaling, RAG, vector databases, evaluation, prompt versioning.

• Technical skills in: OpenTelemetry, Langfuse, Prometheus, Grafana.

• Technical skills in: secrets management, network security, and responsible-AI controls.

• Proficiency in scripting languages: Python, Go, Bash.

• Good to have: Experience with MLflow, model registries, feature stores, Databricks, Snowflake, Palantir Foundry.

• 6–9 years of relevant experience is required.

• Must be available during client business hours, including US / EST.

🏝️ Benefits

• Opportunities for hands-on learning.

• Access to cutting-edge tools.

• An inclusive workplace culture.

• Opportunities for skill development and professional growth.

• Mentoring and career advancement opportunities.

• Client-facing experience with global teams.