AI/ML Solution Architect

Predikly hiring for Gen AI & ML Architect.

Job Location : Shivajinagar, Pune

Mode : Hybrid

ROLE PURPOSE

As Gen AI & ML Architect, you will own the end-to-end design, development, and delivery of  production-grade Gen AI and Machine Learning solutions for Predikly's clients. You will lead a  team of 5-10 AI/ML engineers, drive technical architecture decisions, and serve as a hands-on  contributor writing code, reviewing models, and solving complex problems alongside your team. This  is a player-coach role demanding both individual technical depth and team leadership maturity. 

KEY RESPONSIBILITIES

1. Team Leadership & Mentoring 

• Lead, manage, and mentor a team of 5-10 Gen AI & ML Developers, driving accountability,  growth, and technical excellence. 
• Conduct code reviews, design reviews, and sprint ceremonies; set engineering standards and  best practices. 
• Collaborate with product managers, delivery leads, and client stakeholders to define roadmaps  and prioritise backlogs. 
• Foster a culture of experimentation, continuous learning, and engineering rigour within the  AI/ML team. 

2. Agentic AI Development 

• Design and build production-grade agentic AI systems for real customer use cases — including multi-agent orchestration, autonomous reasoning pipelines, and tool-use patterns. Architect and implement solutions using frameworks such as LangChain, LangGraph,  AutoGen, CrewAI, and custom agent orchestration layers. 
• Define agent memory, planning, reflection, and tool-call patterns aligned with real-world  reliability requirements. 
• Ensure agents meet production-readiness criteria: latency SLAs, cost budgets, fallback  handling, and observability. 

3. Gen AI & LLM Engineering 

• Work hands-on with leading LLM providers: OpenAI (GPT-4o, o1), Anthropic Claude, Google  Gemini, and open-source models (LLaMA, Mistral). 
• Design and implement Retrieval-Augmented Generation (RAG) pipelines with hybrid search,  re-ranking, and context compression. 
• Engineer advanced prompting strategies: chain-of-thought, structured output, few-shot, system  persona, and dynamic routing. 
• Build vector search & embedding pipelines using tools such as Pinecone, Weaviate, Qdrant,  pgvector, and FAISS. 
• Evaluate, fine-tune, and benchmark models; manage prompt versioning and model drift  monitoring. 

4. Machine Learning Engineering 

• Design and deliver end-to-end ML pipelines: data ingestion, feature engineering, model  training, evaluation, and deployment.
• Apply ML techniques including classification, regression, clustering, NLP, time-series  forecasting, and transformer-based architectures. 
• Leverage MLflow, Weights & Biases, or Kubeflow for experiment tracking, model registry,  and pipeline orchestration. 
• Implement monitoring and feedback loops for model drift, data drift, and retraining triggers in  production. 

5. Cloud Architecture & Infrastructure 

• Architect and deploy AI/ML workloads on AWS, Azure, and GCP using managed AI services  and custom containerised deployments. 
• Use AWS SageMaker, Azure ML, Google Vertex AI, Lambda, ECS, and serverless patterns  to build scalable AI services. 
• Design secure, cost-optimised cloud architectures; apply IAM, VPC, secrets management, and  cost tagging best practices. 
• Set up CI/CD pipelines for ML workflows using GitHub Actions, Azure DevOps, or AWS  CodePipeline. 

6. Full-Stack AI Application Development 

• Contribute to and guide development of AI-powered web applications using React, Angular,  Next.js on the frontend and Node.js on the backend. 
• Design and manage databases including PostgreSQL, MySQL (relational) and MongoDB,  DynamoDB, Redis, Elasticsearch (NoSQL). 
• Build REST and GraphQL APIs that surface AI capabilities to end-user products; ensure API  security, versioning, and documentation. 
• Integrate AI/ML model outputs into product UIs with appropriate UX patterns for streaming,  latency masking, and error handling. 

7. Client Orientation & Stakeholder Engagement 

• Serve as the primary technical interface for client engagements — leading discovery  workshops, solution walkthroughs, and sprint demos to build confidence and trust with  business and technical stakeholders. 
• Translate complex AI/ML concepts, architectural trade-offs, and model limitations into clear,  business-relevant language that non-technical clients and product owners can act on. • Collaborate with client teams to identify high-impact AI use cases, define success metrics,  and ensure delivered solutions align with stated business outcomes — not just technical  specifications. 

REQUIRED QUALIFICATIONS & EXPERIENCE

Experience 

• 8–10 years of total software/AI engineering experience. 
• Minimum 3 years in hands-on Generative AI & ML project delivery in production  environments. 
• Proven experience leading a team of 5-10 AI/ML developers with direct responsibility for  delivery, quality, and team development. 
• Demonstrated track record of shipping agentic AI systems to real customers — not just  PoCs or prototypes. 
• Experience working in an Agile/Scrum delivery model, including sprint planning, stand-ups,  retrospectives, and stakeholder demos. \

Education

• B.E. / B.Tech / M.Tech in Computer Science, AI/ML, Data Science, or a related technical  discipline.

Interested please share resume on [email removed]