AI/ML Engineer-Architect

Role & Responsibilities

Role Summary

We are looking for a highly skilled AI Engineer / AI Architect to design, develop, and deploy scalable AI solutions. The ideal candidate will have strong expertise in machine learning, deep learning, and Generative AI, along with the ability to architect end-to-end AI systems aligned with business objectives.

Key Responsibilities

• Design and implement end-to-end AI/ML solutions from data ingestion to deployment.
• Build and optimize machine learning and deep learning models for real-world applications.
• Architect scalable and secure AI systems using cloud platforms.
• Work on Generative AI use cases including LLMs, prompt engineering, and fine-tuning.
• Collaborate with cross-functional teams to translate business requirements into AI solutions.
• Develop and manage data pipelines and model lifecycle (MLOps).
• Ensure model performance, scalability, reliability, and monitoring in production.
• Stay updated with emerging AI trends and evaluate new tools/technologies.
• Mentor junior engineers and guide best practices in AI development.

Ideal Candidate

• Strong AI Architect Profile with end-to-end ML / Deep Learning / GenAI ownership
• Mandatory (Experience 1) – Must have 6+ years of engineering experience, with atleast 4+ years in AI/ML, or Machine Learning Engineering.
• Mandatory (Experience 2) – Must have atleast 3+ years of hands-on experience building and deploying Generative AI solutions, including LLMs, RAG, AI agents, fine-tuning, prompt engineering, and related technologies.
• Mandatory (Tech skill 1) – Must have worked at architecture level (not engineering-only), designing end-to-end, scalable, secure AI systems through deployment
• Mandatory (Tech skill 1) – Must have strong proficiency in Python along with AI or ML libraries such as TensorFlow, PyTorch and Scikit-learn.
• Mandatory (Tech skill 2) – Must have hands-on experience with Machine Learning, Deep Learning and NLP, including model fine-tuning and LLMs.
• Mandatory (Tech skill 4) – Must have experience with MLOps tooling (MLflow, Kubeflow or Azure ML), a cloud platform (Azure, AWS or GCP), and containerization (Docker, Kubernetes).
• Mandatory (Mentoring) – Must have led / mentored AI engineers (architect-level leadership)
• Mandatory (Communication) – Must have strong stakeholder management and requirement-gathering experience with US or UK clients.
• Mandatory (Company) – Must come from a B2B IT services or IT consulting background
• Mandatory (Note 1) – CTC is inclusive of 5% variable
• Preferred (Data) – Data engineering concepts, working with large datasets, APIs & microservices
• Preferred (Production) – Experience on production-grade AI systems; open-source AI contributions
• Preferred (Tools) – Exposure to Azure Foundry and Databricks Agent Bricks.
• Preferred (Certifications) – Certifications in AI, ML or cloud platforms.

Perks, Benefits and Work Culture

• The company provides free AWS and Azure certification training — no cost is borne by the candidate
• Group medical insurance of ₹5 lakhs is included in the benefits package, along with meal allowances

Skills: learning,deep learning,ml,tuning,aws,azure,machine learning,cloud