AIML Architect

Role:

About the Role

10+ years of hands-on experience

Key Responsibilities

• Lead the design, development, and deployment of

  AI/ML and GenAI solutions

  (LLMs, RAG architectures, vector databases, orchestration frameworks).

• LiteLLM

  : with the Python SDK,.

• Opik: Open-Source LLM Evaluation & Observability

  :
• Drive

  enterprise-scale AI adoption

  across business functions (AML compliance, customer experience, predictive analytics, intelligent automation).
• Architect and implement

  cloud-native AI solutions

  on AWS, Azure, or GCP, integrating with enterprise systems.
• Lead

  digital transformation programs

  involving AI, cybersecurity (IAM/PAM, Zero Trust), networking, and enterprise applications.
• Manage

  multi-million-dollar programs

  , ensuring delivery excellence, compliance, and measurable ROI.
• Establish and govern

  AI/Tech Centers of Excellence (CoEs)

  , setting best practices for experimentation, deployment, and monitoring.
• Partner with

  CXOs and stakeholders

  to define strategy, build AI roadmaps, and drive change management for adoption.
• Mentor and grow teams of data scientists, engineers, and solution architects in AI and emerging technologies.
• Collaborate with product, risk, and compliance teams to ensure AI solutions meet regulatory and ethical AI standards.
• Continuously explore emerging technologies (GenAI, blockchain, IoT, automation) and assess business applicability.

Key Skills & Competencies

AI/ML & GenAI

• LLMs, RAG (Retrieval-Augmented Generation), LangChain, LlamaIndex, Hugging Face
• Generative AI tools: Microsoft Copilot, OpenAI APIs, Anthropic Claude
• ML frameworks: TensorFlow, PyTorch, Scikit-learn
• Data engineering: ETL pipelines, data lakes, vector DBs (Pinecone, Chroma, Weaviate)

Cloud & Infra

• AWS, Azure, GCP AI/ML services
• OCI, Kubernetes, Docker for scalable deployments

Cybersecurity & Compliance

• IAM/PAM rollouts, Zero Trust security, regulatory audits
• AI model governance, Responsible AI practices

Program & Technology Management

• Agile, SAFe, Scrum methodologies
• PMO governance, vendor management, ROI tracking
• Stakeholder engagement, business storytelling

Qualifications

• Bachelors or Master’s degree in Computer Science, Engineering, AI/ML, or related field.
• 10+ years in technology leadership roles, with at least 5 years in

  hands-on AI/ML/GenAI solution delivery

  .
• Strong exposure to BFSI, Telecom, Healthcare, or Public Sector domains.
• Certifications: PMP, SAFe, AWS/Azure AI certifications, ITIL, or equivalent preferred.