Description

Description:

We are seeking a GenAI Solution Architect to lead the design and implementation of scalable, secure, and responsible AI/ML solutions. Inthis role, you'll bridge business needs with technical execution, architecting backend systems, enabling agentic RAG, and ensuring AI adoption is both innovative and compliant. You'll work across teams to design cloud-native AI

architectures, optimize costs, and embed observability and responsible Ai practices into every solution.

Key Responsibilities

Primary Skills: Cognigy will be a huge plus

Design and implement

backend architectures for scalable GenAI solutions.

Build agentic RAG pipelines with strong context management.

Lead model grounding, fine-tuning, and distillation for enterprise AI.

Implement observability and monitoring frameworks for AI in production.

Drive responsible AI practices, aligning with risk and compliance policies.

Integrate GenAI frameworks, cloud services, and container orchestration.

Optimize AI deployments for scalability, resilience, and cost efficiency.

Lead agile project delivery, mentoring technical teams.

Communicate architectural decisions clearly to both technical and business leaders.

Required Qualifications

15+ years of experience in building and scaling software systems.

Proven expertise in backend and distributed cloud-native architectures.

Hands-on experience with Generative AI techniques: agentic RAG, prompt context, grounding, fine-tuning.

Strong knowledge of responsible AI, risk, and compliance frameworks.

Proficiency in containerization (Docker) and orchestration (Kubernetes).

Experience with Cognigy.AI or similar conversational AI platforms.

Skilled in cloud platforms

(AWS, Azure, GCP) for AI deployments.

Familiarity with GenAI frameworks (e.g., LangChain, LlamaIndex, Haystack).

Experience with MLOps, CI/CD, and AI observability.

Excellent communication and leadership skills with experience in agile delivery.

Strategic thinker with strong problem-solving and analytical abilities.

Preferred Qualifications

Experience with model distillation and cost optimization.

Background in multi-cloud or hybrid AI deployments.

Knowledge of vector databases, retrieval pipelines, and embeddings.

Certifications in AI/ML, cloud architecture, or responsible AI

Key Skills

Education

Any Graduate