Job Description

Roles & Responsibilities

We are seeking AI / ML Engineers across multiple experience levels (T1 T5) to design, develop, train, deploy, and optimize machine learning models and AI solutions throughout the complete machine learning lifecycle. Candidates will work on data preparation, feature engineering, model development, evaluation, deployment, monitoring, and continuous improvement using modern cloud AI platforms and open-source machine learning frameworks. The role offers opportunities ranging from entry-level implementation to enterprise AI architecture and technical leadership.

Key Responsibilities

• Design, develop, train, evaluate, and deploy machine learning and AI solutions.
• Build scalable ML pipelines from data preparation through production deployment.
• Develop supervised, unsupervised, deep learning, and generative AI models.
• Perform feature engineering, data preprocessing, model validation, and hyperparameter optimization.
• Integrate ML models into enterprise applications and cloud-native environments.
• Deploy AI models using managed cloud ML services and MLOps practices.
• Monitor model performance, drift, accuracy, and production reliability.
• Collaborate with Data Scientists, Data Engineers, Software Engineers, and DevOps teams.
• Optimize model performance, scalability, and inference latency.
• Document models, experiments, evaluation metrics, deployment processes, and governance standards.
• Follow AI security, responsible AI, and model governance best practices.

Required Technical Skills

• Cloud AI Platforms: GCP Vertex AI or BigQuery ML or Dataflow, Azure ML or Azure OpenAI, AWS SageMaker or Amazon Bedrock
• Programming: Python
• Machine Learning Frameworks: TensorFlow or PyTorch
• Generative AI & LLM Frameworks: HuggingFace or LangChain
• Data & Analytics: Databricks
• Additional Skills: Machine Learning, Deep Learning, NLP, Computer Vision, Model Evaluation, Feature Engineering, API Development, Git

Responsibilities by Tier

T1 Associate AI / ML Engineer (0 - 2 Years)

• Role Focus: Learning, implementation, and execution under supervision.
• Responsibilities
  • Assist in data preparation, cleansing, and feature engineering.
  • Develop simple machine learning models using established frameworks.
  • Support model training, testing, and validation activities.
  • Deploy models under senior guidance.
  • Maintain documentation for datasets, experiments, and models.
  • Debug ML pipelines and resolve basic issues.
  • Learn cloud AI platforms and development best practices.
  • Follow coding standards, security policies, and project guidelines.

T2 AI / ML Engineer (2 - 4 Years)

• Role Focus: Independent development and delivery.
• Responsibilities
  • Build and deploy production-ready machine learning models.
  • Perform feature engineering and model optimization.
  • Develop reusable ML components and inference APIs.
  • Implement model evaluation and performance monitoring.
  • Integrate ML models into enterprise applications.
  • Collaborate with cross-functional engineering teams.
  • Troubleshoot production AI issues.
  • Contribute to model documentation and deployment automation.

T3 Senior AI / ML Engineer (5 - 7 Years)

• Role Focus: Technical ownership and solution development.
• Responsibilities
  • Design end-to-end AI and machine learning solutions.
  • Lead development of complex ML pipelines and AI applications.
  • Optimize training pipelines for performance and scalability.
  • Guide junior engineers and perform technical reviews.
  • Implement Responsible AI, explainability, and governance practices.
  • Improve model reliability, monitoring, and lifecycle management.
  • Collaborate with business stakeholders to translate requirements into AI solutions.
  • Support architecture decisions for enterprise AI initiatives.

T4 Lead AI / ML Engineer (8 - 11 Years)

• Role Focus: Technical leadership and enterprise solution delivery.
• Responsibilities
  • Lead architecture and delivery of enterprise AI platforms and machine learning solutions.
  • Define technical standards, reusable frameworks, and engineering best practices.
  • Lead multiple AI initiatives across business domains.
  • Drive cloud-native AI solution design and deployment.
  • Review solution architecture, model performance, and production readiness.
  • Mentor engineering teams and provide technical leadership.
  • Collaborate with enterprise architects, product owners, and business leaders.
  • Improve AI platform scalability, governance, security, and operational excellence.

T5 Principal AI / ML Architect (12+ Years)

• Role Focus: Enterprise AI strategy, architecture, and innovation.
• Responsibilities
  • Define enterprise AI/ML strategy and long-term technology roadmap.
  • Own architecture decisions for large-scale AI and machine learning platforms.
  • Lead enterprise-wide AI transformation initiatives.
  • Establish standards for Responsible AI, governance, security, and compliance.
  • Evaluate emerging AI technologies, frameworks, and cloud services.
  • Drive innovation in Generative AI, LLMs, and advanced machine learning solutions.
  • Provide executive-level technical guidance and strategic recommendations.
  • Lead technical communities, architecture reviews, and cross-functional AI governance.
  • Influence organizational AI adoption and engineering excellence across multiple programs.

Preferred Certifications

• One or more of the following certifications is highly preferred: Google Professional Machine Learning Engineer, AWS Certified Machine Learning Specialty, Microsoft Certified: Azure AI Engineer Associate, TensorFlow Developer Certificate

Desired Candidate Profile

Preferred Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, or a related field.
• Strong understanding of statistics, machine learning algorithms, deep learning, and generative AI concepts.
• Experience with cloud AI platforms and modern ML frameworks.
• Knowledge of MLOps, CI/CD, model deployment, and production monitoring is an advantage.
• Strong analytical, communication, and problem-solving skills.
• Ability to work in Agile, cross-functional, and enterprise-scale environments.