Senior Machine Learning Engineer
Description -
We are looking for a Senior MLOps Engineer to design, build, and operate the infrastructure that enables machine learning models and large language models to be deployed safely, reliably, and at scale.
In this role, you will create the end-to-end capabilities required to move models from experimentation into production, expose them through secure and highly available endpoints, and enable users and applications to interact with AI-powered services.
You will work across AWS and Databricks to establish robust CI/CD pipelines, model-serving infrastructure, observability, governance, rollback mechanisms, and operational standards. You will partner closely with data scientists, machine learning engineers, software engineers, security teams, and platform engineers.
The ideal candidate combines strong cloud and DevOps engineering skills with a practical understanding of machine learning systems, LLM deployment patterns, and production reliability.
Key Responsibilities
MLOps Platform and Architecture
Design and implement a scalable MLOps platform using AWS and Databricks.
Define reference architectures and reusable deployment patterns for traditional machine learning models, deep learning models, and large language models.
Build standardized workflows that move models from development and validation into staging and production.
Develop self-service capabilities that allow data scientists and ML engineers to deploy models without manually managing infrastructure.
Establish clear separation between development, testing, staging, and production environments.
Design multi-region or multi-availability-zonearchitectures where required by business continuity and availability objectives.
CI/CD and Model Deployment
Build automated CI/CD pipelines for model code, inference services, infrastructure, configuration, and model artifacts.
Implement automated testing across the deployment lifecycle, including:
Unit testing
Integration testing
Model validation
Data contract validation
API and endpoint testing
Security testing
Performance and load testing
Regression testing
Automate model packaging, containerization, versioning, approval, promotion, and deployment.
Support deployment strategies such as blue-green deployments, canary releases, shadow deployments, and controlled traffic shifting.
Implement reliable rollback and roll-forward mechanisms for application code, infrastructure, model versions, prompts, and configuration.
Ensure deployments are reproducible, auditable, and recoverable.
Model and LLM Serving
Design and operate secure, scalable, low-latency inference endpoints.
Deploy models using appropriate services and patterns across AWS and Databricks, such as:
Databricks Model Serving
MLflow Model Registry
Amazon SageMaker
Amazon ECS or EKS
AWS Lambda, where appropriate
API Gateway
Application Load Balancers
Build synchronous, asynchronous, batch, and streaming inference capabilities.
Design serving architectures for LLM-powered applications, including:
Hosted foundation models
Open-source models
Fine-tuned models
Retrieval-augmented generation
Embedding services
Vector search
Prompt and response orchestration
Tool-calling and agentic workflows
Optimize inference performance, scalability, GPU utilization, concurrency, throughput, latency, and cost.
Implement autoscaling, request throttling, queuing, caching, timeout handling, and graceful degradation.
Reliability, Recovery, and Business Continuity
Build recoverable model-serving endpoints with clearly defined recovery time and recovery point objectives.
Implement automated health checks, failover mechanisms, retry policies, circuit breakers, and service recovery procedures.
Design backup and recovery processes for:
Model artifacts
Model registry metadata
Feature definitions
Deployment configurations
Infrastructure state
Prompts and application configuration
Vector indexes and knowledge-base assets
Create disaster recovery procedures and regularly test restoration and failover capabilities.
Ensure production services can recover from failed deployments, infrastructure outages, model errors, and upstream dependency failures.
Develop operational runbooks and incident response procedures.
Monitoring and Observability
Implement end-to-end observability for infrastructure, applications, models, data, and user interactions.
Monitor:
Availability
Request volume
Latency
Error rates
Resource utilization
Model performance
Data quality
Data drift
Concept drift
Prediction distributions
LLM response quality
Hallucination and safety indicators
Token consumption
Cost per request
Establish dashboards, alerts, service-level indicators, and service-level objectives.
Integrate monitoring with incident management and on-call processes.
Enable traceability from user requests through model inference, retrieval, orchestration, and downstream services.
Support root-cause analysis by maintaining structured logs, metrics, traces, model lineage, and deployment history.
Security and Governance
Implement security controls for model-serving environments, APIs, data access, and deployment pipelines.
Apply least-privilege access using AWS IAM, Databricks permissions, service principals, and role-based access control.
Secure secrets, credentials, API keys, certificates, and tokens using approved secrets-management solutions.
Implement encryption in transit and at rest.
Design private networking, endpoint controls, firewall rules, and secure connectivity patterns.
Support authentication, authorization, rate limiting, and tenant isolation for AI services.
Ensure models and LLM applications comply with organizational requirements for privacy, security, auditability, and responsible AI.
Maintain model lineage, approval records, version history, and deployment audit trails.
Implement controls for sensitive data, personally identifiable information, prompt injection, unsafe outputs, and unauthorized model access.
Infrastructure as Code and Automation
Build and maintain cloud infrastructure using Infrastructure as Code tools such as Terraform or AWS CloudFormation.
Automate environment provisioning, policy enforcement, deployment configuration, and platform upgrades.
Create reusable modules, templates, libraries, and deployment frameworks.
Implement configuration management and environment-specific parameterization.
Ensure infrastructure changes are peer-reviewed, tested, version-controlled, and traceable.
Collaboration and Engineering Standards
Partner with data scientists and ML engineers to productionize models and define deployment requirements.
Work with software engineering teams to integrate model endpoints into user-facing products and internal applications.
Collaborate with cybersecurity, architecture, legal, privacy, and governance teams.
Define MLOps engineering standards, design principles, coding practices, and operational requirements.
Conduct architecture reviews, code reviews, and production-readiness assessments.
Machine Learning Experience :
Significant experience in MLOps, platform engineering, DevOps, site reliability engineering, cloud engineering, or production machine learning.
Proven experience deploying and operating machine learning models in production.
Strong hands-on experience with AWS services and cloud architecture.
Strong hands-on experience with Databricks, including MLflow, model registries, jobs, clusters, permissions, and model-serving capabilities.
Experience building CI/CD pipelines using tools such as GitHub Actions, GitLab CI/CD, Jenkins, Azure DevOps, or AWS CodePipeline, Docker and containerized application deployment.
Experience with Kubernetes and managed container platforms such as Amazon EKS or ECS.
Proficiency in Python and experience building production-quality APIs and inference services.
Experience with REST APIs, asynchronous processing, event-driven architectures, and distributed systems.
Strong knowledge of Infrastructure as Code, preferably Terraform.
Experience with model versioning, artifact management, experiment tracking, and deployment promotion workflows.
Experience implementing monitoring, logging, tracing, alerting, and production support processes.
Strong understanding of high availability, disaster recovery, fault tolerance, and rollback strategies.
Knowledge of cloud networking, IAM, secrets management, encryption, and secure software delivery.
Training and inference workflows
Online and batch inference
Model serialization and packaging
Feature engineering and feature consistency
Model validation and evaluation,l drift and data drift
Model explainability and reproducibility
GPU-based model serving
Embeddings and vector databases
Retrieval-augmented generation
Prompt management and versioning
LLM evaluation and guardrails
Token limits, context management, and inference cost optimization
Responsible AI, content safety, and human-in-the-loop controls
This role does not necessarily require the candidate to develop new machine learning algorithms. However, the candidate must be able to understand model behavior, deployment constraints, performance characteristics, and operational risks.
Preferred Qualifications:
Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Engineering, Machine Learning, or a related discipline, or equivalent professional experience.
12+ years of total experience
Experience deploying generative AI or LLM-based applications in production.
Experience with Amazon Bedrock, SageMaker, Databricks Mosaic AI, or similar AI platforms.
Experience with vector-search technologies such as Databricks Vector Search, OpenSearch, Pinecone, Weaviate, Milvus, or pgvector.
Experience with LLM application frameworks such as LangChain, LlamaIndex, Semantic Kernel, or equivalent orchestration tools.
Experience with observability platforms such as Datadog, Grafana, Prometheus, CloudWatch, OpenTelemetry, or Splunk.
Experience applying SRE practices to machine learning and AI systems.
Experience operating platforms in regulated, enterprise, or data-sensitive environments.
Relevant AWS, Databricks, Kubernetes, or cloud architecture certifications.
What Success Looks Like
Within the first 6 to 12 months, the successful candidate will:
Establish a standardized and automated path from model development to production.
Reduce the time required to deploy a new model or LLM service.
Enable repeatable deployments across development, staging, and production.
Provide reliable and secure endpoints through which users and applications can interact with AI.
Implement automated rollback and recovery for failed deployments.
Establish monitoring for service health, model performance, quality, security, and cost.
Improve availability, deployment frequency, change-failure rate, and recovery time.
Create reusable platform components that increase engineering productivity.
Establish production-readiness standards for machine learning and generative AI services.
Example Performance Indicators
Model deployment lead time
Deployment frequency
Percentage of automated deployments
Change-failure rate
Mean time to detect incidents
Mean time to recover
Endpoint availability
P95 and P99 inference latency
Model rollback success rate
Recovery-test success rate
Infrastructure provisioning time
Cost per inference request
Percentage of production models with complete lineage and monitoring
Number of security or compliance exceptions
Internal developer and data scientist satisfaction
Ideal Candidate Profile
You are a pragmatic platform engineer who understands that deploying a model is only the beginning. You think about security, reliability, monitoring, recovery, cost, governance, and the end-user experience from the start.
You are comfortable moving between cloud architecture, infrastructure automation, CI/CD pipelines, Python services, Databricks workflows, model registries, Kubernetes, and production incident management. You can translate experimental AI solutions into dependable services that users can trust.
Job -
Data & Information TechnologySchedule -
Full timeShift -
No shift premium (India)Travel -
Relocation -
Equal Opportunity Employer (EEO)-
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