Job Summary

Job Description :

Experienced AIML Architect to design and implement cutting-edge AI/ML solutions leveraging AWS and other modern technologies. The ideal candidate will have strong expertise in building scalable models, deploying them in production, and driving innovation across multiple industry verticals.

Key Responsibilities

Responsibilities

• Design and implement AI/ML solutions for complex business problems across domains like Banking, Finance, Telecom, Retail, LSH and Technology.
• Develop and optimize Machine Learning and Deep Learning models using frameworks such as TensorFlow, Keras, PyTorch.
• Build and maintain scalable data pipelines and ML workflows using Python, Spark, PySpark, and AWS services.
• Deploy and monitor models on AWS
• Transform prototypes into robust, production-ready solutions.
• Deep expertise in developing, testing, deploying, and monitoring AI applications and infrastructure using Git and robust CI/CD pipelines.
• Work with containerization and orchestration tools like Docker and Kubernetes.
• Apply advanced statistical analysis, text mining, and machine log processing techniques.
• Collaborate with cross-functional teams to ensure alignment with business goals
• Knowledge / Experience on AWS Agentic AI Services
• Bachelor’s/Master’s in engineering (pref comp science with specialization in AI/ML)

Skill Requirements

Technical / Functional Skills: 

• AI/ML Development: TensorFlow, Keras, PyTorch, Scikit-learn, Spark ML.
• Programming: Python (expert), R (working knowledge), Spark.
• Data Handling: Pandas, NumPy, PySpark.
• ML Ops: CI/CD pipelines, model deployment, monitoring.
• Statistical Analysis: Strong foundation in statistics and data-driven decision-making.
• AWS Agentic Workflows: Experience using AWS Bedrock and Agent Core to build AI agents
• Generative AI: Amazon Bedrock (Agents, Knowledge Bases, Guardrails), Agent Core
• DevOps & Tools: Git, CI/CD (Deep Experience with GitHub Actions / AWS CodePipeline), Terraform or CloudFormation (IaC), Artifact Management.
• Domain Knowledge: Exposure to supervised, unsupervised, and reinforcement learning.
• Integration Standards: REST APIs, OData, OpenAPI (Swagger) Specifications, JSON.
• Enterprise identity patterns : IAM roles/SCPs, Cognito federation, Okta SSO

Other Requirements

Core Competencies (Soft Skills):

• Strong communication & presentation- Analytical, client-first mindset
• Leadership & stakeholder management- Ability to work in global, fast-paced environment.
• Provide architectural leadership in client workshops, technical assessments, and solution reviews.
• Advise clients on AI strategy, modernization roadmaps, and responsible AI adoption.