- The Machine Learning domain encompasses the application of algorithms and models to enable computers to learn from and make predictions or decisions based on data. This domain is generally a subset of the AI or Data Domain.

- Collaborate with leaders, business analysts, project managers, IT architects, technical leads, and other engineers, along with internal customers, to understand requirements and develop needs according to business requirements for AI solutions.

- Maintain and enhance existing enterprise services, applications, and platforms using domain-driven design and test-driven development.

- Troubleshoot and debug complex issues; identify and implement solutions.

- Create detailed project specifications, requirements, and estimates.

- Research and implement new AI technologies to enhance current processes, security, and performance.

- Work closely with data scientists and product teams to build and deploy machine learning models, focusing on the technical aspects of model deployment.

- Implement and optimize Python-based ML pipelines for data preprocessing, model training, and deployment.

- Monitor model performance and implement strategies for bias mitigation and explainability. Responsible for ensuring models are scalable and efficient in production environments.

- Write and maintain code for model training and deployment, collaborating with software engineers to integrate models into applications.

- Partner with a diverse team of experts, leveraging cutting-edge technologies to build scalable and impactful AI solutions.

Requirements :

- Skills Required : Python, Databricks, Azure Databricks, ADF, Azure ML, Azure, AWS, GCP, Machine Learning, ML, NLP, Gen AI, Tensorflow, Pytorch, and TensorFlow.

- Bachelor's degree in Computer Science, Computer Engineering, Data Science, Information Systems (CIS / MIS), Engineering, or a related technical discipline, or equivalent experience / training.

- 3+ years of full Software Development Life Cycle (SDLC) experience designing, developing, and implementing large-scale machine learning applications in hosted production environments.

- 3+ years of professional, design, and open-source experience.

Preferred Qualifications - Education and Prior Job Experience :

- Master's degree in Computer Science, Computer Engineering, Data Science, Information Systems (CIS / MIS), Engineering, or a related technical discipline, or equivalent experience / training.

- 5 years of full Software Development Life Cycle (SDLC) experience designing, developing, and implementing large-scale machine learning applications

- Airline Industry experience.

Programming and Backend Development :

- Python (backend services/APIs, automation, testing).

- SQL (advanced querying, window functions, optimization, data modeling fundamentals).

- REST APIs, JSON, service integration patterns.

AI Agents / Agent Frameworks (Must-have) :

- Hands-on experience with at least one enterprise-grade agent framework, such as LangGraph/Crew AI/Autogen (or equivalent agent orchestration frameworks), Copilot Studio agents/OpenAI agents (or equivalent).

- Agent orchestration, tool/function calling, structured outputs, evaluation, and iteration patterns.

Analytics, Reporting, and Dashboards :

- Power BI and/or Tableau.

- Metrics definition, reporting automation, executive-ready dashboards.

Cloud Platforms :

- Microsoft Azure or AWS.

- Experience deploying data/AI workloads in cloud environments.

Data and Integration :

- Data pipelines/ELT concepts, dataset curation for analytics.

- Integration with an enterprise risk assessment and governance workflow platform (tool-agnostic).

AI/ML Fundamentals :

- Applied understanding of ML lifecycle concepts.

- Familiarity with LLM concepts like prompting, context handling, grounding patterns like RAG, Evals, Agent Behavior Monitoring etc.

Governance, Risk, and Regulatory Knowledge :

- Working on awareness of AI/data regulations and governance expectations such as GDPR, the EU AI Act, and similar privacy/AI governance frameworks.

- Understanding of governance controls : evidence capture, auditability, traceability, Observability, exception management, and reporting.

Software Engineering Practices :

- Object-oriented design and clean coding principles.

- Version control (Git) and CI/CD fundamentals.

- Testing practices (unit/integration), documentation, logging/monitoring, operational readiness.

Licenses and Certifications (Preferred, not required) :

- Microsoft Certified : Azure Data Engineer Associate / Azure Developer Associate / Azure AI Engineer Associate.

- AWS Certified Developer - Associate / AWS Certified Data Engineer - Associate (or equivalent AWS certifications).

- Power BI or Tableau certification (if applicable/available).

- Any relevant certifications in data privacy or AI governance/risk (e. g., privacy fundamentals, responsible AI).

Methodologies and Tools (Preferred) :

- Experience working in Agile (Scrum/Kanban) environments.

- Familiarity with DevOps toolchains (e. g., CI pipelines, code quality checks, artifact repos).

Language/Communication Skills :

- Ability to effectively communicate both verbally and in writing with all levels within the organization.

- Physical ability necessary to safely and successfully perform the essential functions of the position, with or without any legally required reasonable accommodations that do not pose an undue hardship.