Job Summary

AI Engineer

Key Responsibilities

Develop AI & GenAI Solutions

Build and deploy machine learning and LLM-based solutions (e.g., RAG, copilots, intelligent agents)
Apply NLP/AI techniques to extract insights and automate workflows 

Productionize AI Models

Develop scalable APIs and microservices for model deployment
Integrate with Enterprise Platforms

Design data pipelines and integrate AI models into cloud platforms and business applications
Collaborate with data engineering teams on data ingestion and transformation 

Ensure Responsible AI & Governance

Align solutions with AI governance, compliance, and ethical standards
Support model transparency, auditability, and risk mitigation 

Collaborate & Deliver

Partner with product owners, architects, and business teams to deliver AI use cases
Contribute to reusable components, engineering standards, and best practices

 

Good to have skill

Implement CI/CD pipelines and ensure monitoring, logging, and performance optimization 

 

Required Qualifications

Bachelor’s or Master’s degree in Computer Science, Engineering, or related field
4+ years of experience in AI/ML Engineering or Software Engineering
Strong programming skills in Python
Hands-on experience with:

Machine Learning frameworks (e.g., PyTorch, TensorFlow, Scikit-learn)
LLMs / GenAI (Azure OpenAI, LangChain, RAG patterns)

Experience building APIs, microservices, and production ML pipelines
Exposure to cloud platforms (Azure preferred) and data pipeline development

 

Key Responsibilities

1. Architect And Develop Comprehensive Machine Learning Solutions Using Tensorflow And Pytorch, Collaborating With The Team To Ensure Timely And Quality Delivery Of Both Products And Sustenance Projects.
2. Serve As A Subject Matter Expert In Machine Learning, Providing Technical Guidance To The Team And Stakeholders, Ensuring Projects Align With Industry Standards And Best Practices.
3. Continuously Upgrade Knowledge By Researching And Integrating New Machine Learning Technologies And Methodologies, Ensuring Solutions Remain Current And Meet Evolving Quality Standards.
4. Mentor And Conduct Training Sessions For Team Members, Ensuring A Robust Knowledge Transfer And A Sufficient Pool Of Skilled Professionals In Machine Learning And Related Technologies.
5. Gather And Analyze Specifications To Deliver Tailored Machine Learning Solutions That Meet The Specific Needs Of The Client Organization, Leveraging A Deep Understanding Of Domain Requirements.
6. Support Competency Development By Envisioning Strategic Propositions, Creating Technical Collaterals, And Performing Market Trend Analyses To Position The Organization As A Leader In Machine Learning.
7. Recommend And Implement Initiatives That Create Client Value, Championing The Adoption Of Industry Best Practices In Machine Learning Deployment And Optimization.

Skill Requirements

1. Expert Proficiency In Machine Learning Frameworks Such As Tensorflow And Pytorch.
2. Advanced Knowledge Of Python And Sql For Developing Data-Driven Solutions.
3. Strong Understanding Of Data Modeling, Feature Engineering, And Algorithm Selection In Machine Learning Projects.
4. Excellent Problem-Solving Skills And Ability To Articulate Complex Technical Concepts To Non-Technical Stakeholders.
5. Proven Experience In Leading Technical Teams And Mentoring Junior Professionals.

Other Requirements

1. Optional But Valuable: Certifications In Machine Learning (E.G., Tensorflow Developer Certificate, Aws Certified Machine Learning Specialty, Or Similar).