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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing, developing, and deploying scalable Machine Learning and AI solutions, with a strong focus on MLOps and LLMOps practices. Proficient in utilizing cloud platforms and frameworks to optimize AI workflows and ensure responsible AI implementation.
Highest-signal resume keywords
Machine Learning Model DevelopmentPython ProgrammingCloud Platform Experience (AWS, Azure)MLOps and LLMOps ImplementationGenerative AI Application Development
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine LearningArtificial IntelligencePredictive ModelingFeature EngineeringData TransformationLarge Language Models (LLMs)Prompt EngineeringSQLModel EvaluationAnomaly Detection
Soft Skills
CollaborationCommunicationProblem-SolvingTechnical Documentation
Tools & Technologies
LangGraphLangChainAutoGenCrewAIAzure AI FoundryAWS CloudWatchMLflowPrometheusGrafanaDocker
Industry Keywords
Supply ChainHealthcareResponsible AIAI GovernanceData Security
Tech Stack
Tools & technologiesAWSAzureCloudDockerGrafanaKubernetesMicroservicesPrometheusPythonSQL
About the role
Key responsibilities & impact- Design, develop, and deploy scalable Machine Learning models and AI solutions for complex Supply Chain challenges
- Build end-to-end ML pipelines for forecasting, optimization, predictive analytics, anomaly detection, and intelligent automation
- Design, build, test, and deploy AI Agents and multi-agent systems using LangGraph, LangChain, AutoGen, CrewAI, or similar frameworks
- Develop intelligent workflows using LLMs, tool integration, memory, and orchestration to automate business processes
- Develop data ingestion, transformation, and feature engineering pipelines for structured, semi-structured, and unstructured enterprise data
- Integrate knowledge repositories, knowledge graphs, vector databases, and intelligent document processing solutions
- Deploy and manage AI and Machine Learning applications on AWS and Microsoft Azure
- Build and maintain MLOps and LLMOps pipelines, including model versioning, CI/CD, automated deployment, monitoring, and retraining
- Evaluate, monitor, and optimize ML models and LLMs for accuracy, latency, cloud resource utilization, reliability, and cost
- Implement responsible AI practices
- Collaborate with Supply Chain stakeholders, Data Scientists, Software Engineers, Cloud Architects, Product Managers, and Digital Transformation teams
- Establish engineering standards, contribute to architecture and code reviews, and create technical documentation
Requirements
What you’ll need- Bachelor’s degree in computer science, Software Engineering, AI, or related field and 7+ years of professional experience in Machine Learning, Artificial Intelligence, Data Science, or AI Engineering
- Alternatively, a master’s degree with relevant industry experience and 5+ years of experience
- Strong hands-on expertise in Python
- Experience designing, developing, deploying, and optimizing scalable Machine Learning and AI solutions in production environments
- Experience with predictive modelling, forecasting, optimization, feature engineering, model evaluation, monitoring, and lifecycle management
- Experience using cloud platforms such as Azure, Databricks, and AWS
- Experience building and deploying Generative AI applications and Agentic AI workflows in production using LangGraph, LangChain, AutoGen, CrewAI, or similar technologies
- Practical experience with Large Language Models (LLMs), Prompt Engineering, RAG, AI evaluation techniques, and responsible AI practices
- Strong understanding of system design patterns, microservices architecture, APIs, Docker, Kubernetes, and infrastructure automation
- Experience implementing AI observability and evaluation using tools such as Azure AI Foundry, Azure Monitor, Azure ML Monitoring, AWS CloudWatch, MLflow, LangSmith, Prometheus, Grafana, or OpenTelemetry
- Experience with enterprise data platforms, data pipelines, SQL, and distributed data processing frameworks
- Willingness to travel 10–20%
- Preferred: hands-on experience developing AI/ML solutions for Supply Chain, Healthcare, or other enterprise domains
- Preferred: experience implementing MLOps and LLMOps practices
- Preferred: familiarity with AI governance, model explainability, data security, privacy, and Responsible AI practices
Benefits
Comp & perks- Hybrid work arrangement in Bangalore
- Opportunities to work on innovative healthcare AI, machine learning, and data science solutions
- Collaboration with cross-functional technical and business teams
- Exposure to AWS and Microsoft Azure cloud platforms
- Professional work in AI/ML, Generative AI, Agentic AI, MLOps, and LLMOps