Technical AI Product Manager (Agentic AI) at Timble Technologies · Delhi, Gurugram, Noida, Ghaziabad, Faridabad · 4 - 20 years · ₹4L - ₹20L / yr · Raised funding · Posted 17 Jun 2026
Python
Machine Learning (ML)Job Title: Technical AI Product Manager (Agentic AI)
Location- Delhi
Job type: Full time, On site
About Us: TIMBLE is leading Authentication Company, delivering cutting edge technology and alternate data analysis for Identity management, Onboarding & Verification and Business Intelligence. We provide solutions across three verticals
1. BFSI Solutions
2. KYC and background check Solutions
3.AI Solutions
Key Responsibilities
- Product Strategy & Roadmap: Own the end-to-end product lifecycle for AI-native and agentic products. Translate complex operational problems into AI-driven workflows and autonomous agent solutions.
- Agentic AI Architecture: Prototyping multi-agent workflows using LangGraph, LangChain, and modern LLM tooling. Build systems featuring tool calling, structured memory, planning, and reflection capabilities.
- Hands-on Development: Develop proof-of-concepts (PoCs) and MVPs using Python. Integrate LLM APIs, vector databases, retrieval systems, and custom prompt engineering techniques.
- Agile Execution: Convert stakeholder requirements into technical specifications, user stories, and high-quality PRDs. Lead sprint planning and agile ceremonies with backend and AI/ML teams.
- Performance & Metrics: Define and monitor AI-specific KPIs, including token latency, system reliability, contextual accuracy, and hallucination rates.
Required Skills & Qualifications
- Experience: 4+ Years in Product Management or Technical Product Management (TPM).
- Domain Expertise: Proven track record of shipping AI/ML, Generative AI, or LLM-based applications.
- Technical Skills: Hands-on experience prototyping or designing AI agents/workflows. Strong programming literacy in Python to interface with data science frameworks.
- Education: B.Tech/M.Tech in Computer Science or a related technical discipline from a reputed institute.
What We Offer
- Absolute ownership of the core agentic roadmap in a high-growth AI startup.
- A collaborative, high-velocity workspace with zero corporate bureaucracy.
- Competitive compensation packages and performance-driven trajectory.
Learn more about us at: https://timbleglance.com
Shubham Vishwakarma
Full Stack Developer - Averlon
About Timble Technologies
About
Timble technology is the fastest growing IT company dealing in Artificial Intelligence, Speech Recognition, Facial Recognition, Cyber Security, Bespoke Solutions in Delhi. Timble technology is achieving success and getting more and more reputation in the field of IT
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PythonJob Description:
We are looking for a hands-on AI Engineer with experience in Generative AI and Agentic AI to build and deploy production-ready AI solutions.
Key Responsibilities:
- Develop and deploy GenAI and Agentic AI applications.
- Build RAG pipelines, LLM workflows, and AI agents.
- Develop solutions using Python, LangChain, LangGraph, LlamaIndex, or similar frameworks.
- Implement tool calling, context retrieval, and LLM orchestration.
- Integrate AI solutions with APIs and cloud platforms.
- Work with AWS/Azure/GCP, Docker, and CI/CD.
Required Skills:
- Strong Python programming skills.
- 3+ years of GenAI/Agentic AI experience.
- RAG and LLM orchestration.
- LangChain / LangGraph / LlamaIndex / AutoGen / CrewAI / Semantic Kernel.
- MCP and A2A knowledge.
- Cloud, APIs, Docker, and CI/CD experience.
Preferred Experience:
Hands-on experience building and deploying production-ready AI solutions.
PythonAI Engineer
LLMs, Agents & AI Services
📍 Mumbai (On-site) | Full-time | 2-4 years
About the Role:
Unico Connect is an AI-first technology partner that builds custom mobile, web, and AI products for clients across multiple geographies.
AI is core to how we design, deliver, and scale software for our customers.
We are hiring an AI Engineer for a dedicated client engagement building a complex production AI platform, working on the AI capabilities and agentic features at the core of the product.
The mandatory requirement for this role is at least one AI feature personally shipped to production for real users, with operational ownership.
The role suits someone who thinks quickly on solutioning, can take an ambiguous problem to a working prototype in days, and has the discipline to carry it through to production with predictable economics.
You will work alongside the Senior AI Engineer and the wider pod, with ownership of parts of the AI surface area of the product.
Responsibilities:
Solutioning and POCs
Translate ambiguous customer problems into working POCs at speed.
Pick the right model, framework, and architecture, and demonstrate value early before scaling investment.
LLM Application Development
Build AI features and services using LLM APIs from OpenAI, Anthropic, Google, and self-hosted open-weight models (Llama, Qwen, Mistral).
Choose the right model per use case based on cost, latency, capability, and context-window trade-offs.
Agentic System Design
Design and implement agentic workflows using LangGraph, CrewAI, AutoGen, LlamaIndex Agents, or custom orchestration.
Cover tool use, planning, memory, and multi-step reasoning appropriate to the problem.
API and Service Development
Build production AI services and APIs using Python and FastAPI.
Handle streaming responses, async processing, structured outputs, retries, and graceful degradation when models or tools fail.
Retrieval and Tool Integration
Implement RAG pipelines with vector databases (Pinecone, Weaviate, Qdrant, pgvector, Chroma), embeddings, chunking strategies, hybrid search, and reranking.
Integrate external tools, internal APIs, and document sources through tool-calling and MCP-style patterns.
Cost Analysis and Unit Economics
Model the per-request and per-user cost of every AI feature before it ships.
Track token usage, prompt caching, batching, and model-routing strategies.
Drive measurable improvements in unit economics.
Production Hardening
Add observability and tracing (LangSmith, Langfuse, OpenTelemetry), guardrails, content safety checks, prompt injection defences, and fallback behaviour.
Prompt Engineering and Evaluation
Design, test, and iterate prompts with measured outcomes.
Build evaluation harnesses for accuracy, hallucination, latency, and cost.
Run benchmarks across models and prompt variants before locking in a design.
Requirements:
AI Feature Shipped to Production (Mandatory)
Must have personally built and shipped at least one AI feature that runs in production for real users, with operational ownership.
POCs, internal demos, and one-off scripts do not qualify.
2 to 4 Years of Professional Software or AI Engineering Experience
With at least one production AI feature owned end to end.
Strong Python Proficiency and API Development with FastAPI
Comfort with type hints, async, packaging, testing, streaming responses, and authentication.
Production-grade Python, not notebook-only code.
Hands-on Depth Across the LLM and Agent Stack
Working experience with at least two of OpenAI, Anthropic Claude, Google Gemini, or self-hosted open-weight models (vLLM, Ollama, Together, Replicate).
Working familiarity with at least one agent framework (LangGraph, CrewAI, AutoGen, LlamaIndex Agents) or hand-rolled equivalent.
Working knowledge of RAG, embeddings, and vector databases (Pinecone, Weaviate, Qdrant, pgvector, Chroma).
Solutioning Speed and POC Velocity
Demonstrated ability to move from a fuzzy problem to a working prototype in days.
Strong instinct for what to build first, what to defer, and what to throw away.
Cost Discipline for Production AI
Ability to calculate, monitor, and optimise the cost of LLM APIs, tokens, embeddings, vector store usage, and infrastructure.
Treats unit economics as a first-class concern.
AWS Familiarity
Working knowledge of EC2, S3, IAM, and at least one of Bedrock, SageMaker, or equivalent.
Comfortable in a Fast-Moving Environment
Self-directed, comfortable with ambiguity, takes ownership without being asked, and ships under shifting priorities.
Strong Written and Spoken English Communication
Able to explain trade-offs to non-AI engineers, designers, product managers, and clients in plain language.
Nice to Have
- fine-tuning or LoRA, QLoRA, PEFT exposure
- MCP server authoring
- eval framework experience (LangSmith, Promptfoo, Ragas, DeepEval)
- open-source AI contributions
- multi-modal models (vision, audio)
Senior Product Manager – AI / B2B SaaS
Location: Bengaluru
Work Mode: Work from Office
Experience: 5+ years
Department: Product Management
About LeadSquared
LeadSquared is a leading Sales CRM for high-velocity revenue teams, helping businesses manage leads, sales processes, customer interactions, marketing, and revenue workflows at scale.
We serve businesses where large sales teams manage high volumes of leads and customer interactions, making speed, prioritisation, automation, and effective sales execution critical to revenue growth.
Role Overview
We are looking for a Senior Product Manager – AI to own the roadmap and end-to-end execution of AI products within LeadSquared.
Key Responsibilities
- Own the roadmap, prioritisation, and end-to-end execution for your AI product area.
- Understand customer problems and identify where AI can create meaningful, scalable value and translate them into clear product requirements and scalable solutions.
- Drive products from discovery and 0→1 development through launch, adoption, and iteration.
- Build and scale end-user-facing AI experiences that work reliably within enterprise workflows.
- Make informed product decisions considering quality, latency, and scale trade-offs in production AI systems.
- Work closely with Engineering, Design, AI/ML, Data, and GTM teams to ship high-quality products.
- Define and track product adoption, engagement, customer outcomes, and business impact.
- Drive GTM, adoption, and continuous product optimisation.
What We're Looking For
- 5+ years of Product Management experience.
- 2+ years of hands-on Product Management experience building AI products.
- Strong B2B SaaS product experience.
- Experience in 0→1 product development and launches.
- Strong product discovery, prioritisation, roadmap management, and execution skills.
- Familiarity with AI evaluations (evals), observability, guardrails, and human oversight.
- Data-driven approach with strong understanding of product metrics.
- Excellent communication, problem-solving, and stakeholder management skills.
- Candidates from Tier 1 / Tier 1.5 / Tier 2 colleges preferred.
Good to Have
- Experience building products using GenAI, LLMs, RAG, AI Agents / Agentic AI, or ML-based decision systems.
- Experience in CRM, SalesTech, MarTech, or Enterprise SaaS.
- Experience building AI products for sales, revenue, operations, or other enterprise users.
- Experience taking an AI capability from an early prototype to a reliable, scalable enterprise product.
- Candidates who have built AI side projects will have a preference. Share links to live products, prototypes, demos, GitHub repositories, agents, or apps you’ve built.
About LeadSquared
LeadSquared is a leading sales execution and marketing automation platform trusted by 2,000+ businesses globally, including healthcare, education, financial services, and real estate. Headquartered in Bengaluru with offices across the US, UK, UAE, and Southeast Asia, we empower sales teams to close faster, smarter, and at scale.
Our AI team is at the forefront of integrating cutting-edge large language model capabilities into enterprise workflows — building intelligent agents, copilots, and automation systems that redefine how businesses operate.
Role Overview
We are looking for a Senior AI Engineer with hands-on experience building LLM-powered agents and agentic AI systems. You will design, develop, and deploy autonomous AI pipelines that solve complex, multi-step business problems — from lead qualification and follow-up automation to intelligent CRM workflows and beyond.
This role is ideal for someone who is deeply excited about the frontier of AI, can move fast, and wants their work to directly impact millions of sales professionals worldwide.
Key Responsibilities
•
Design and build LLM-powered agentic systems using frameworks such as LangChain, LlamaIndex, AutoGen, or CrewAI to automate complex, multi-step workflows.
•
Develop and maintain Retrieval-Augmented Generation (RAG) pipelines with vector databases (Pinecone, Weaviate, Chroma, pgvector) for domain-specific knowledge grounding.
•
Build and integrate tool-use and function-calling capabilities into AI agents, enabling dynamic interaction with internal APIs, databases, and third-party services.
•
Implement prompt engineering strategies including chain-of-thought, few-shot prompting, and structured output parsing to ensure reliable agent behavior.
•
Design evaluation frameworks and observability pipelines (LangSmith, Helicone, custom metrics) to monitor agent performance, accuracy, and cost.
•
Collaborate with product, sales, and domain teams to translate business requirements into AI-driven solutions and features.
•
Optimize LLM inference for latency and cost using techniques like caching, model distillation, quantization, and batching.
•
Stay current with the rapidly evolving LLM ecosystem and proactively propose improvements and new approaches.
•
Contribute to internal best practices, documentation, and knowledge-sharing across the engineering org.
Required Qualifications
Experience
•
2–4 years of professional software engineering experience, with at least 1–2 years focused on LLM/AI systems.
•
Proven experience shipping LLM-based products or agentic AI systems into production environments.
Technical Skills
•
Strong proficiency in Python and familiarity with async programming patterns for AI pipelines.
•
Hands-on experience with LLM APIs: OpenAI (GPT-4o), Anthropic (Claude), Google (Gemini), or open-source models (Llama, Mistral).
•
Experience with agentic frameworks: LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI, or similar.
•
Solid understanding of RAG architectures, embedding models, and semantic search.
•
Experience with vector databases and similarity search infrastructure.
•
Knowledge of REST APIs, microservices architecture, and containerization (Docker/Kubernetes).
Problem-Solving & Mindset
•
Strong ability to decompose ambiguous, open-ended problems into structured AI system designs.
•
Experience with prompt debugging, LLM evaluation, and iterative refinement workflows.
•
Ability to balance research exploration with engineering pragmatism to ship reliable systems.
Preferred Qualifications
•
Experience with multi-agent orchestration and agent memory systems (short-term and long-term).
•
Familiarity with fine-tuning or RLHF workflows for domain adaptation.
•
Background in NLP, information retrieval, or conversational AI.
•
Prior experience in B2B SaaS or CRM domain is a plus.
•
Contributions to open-source AI/ML projects or published research/blogs.
•
Experience with cloud platforms: AWS, GCP, or Azure — particularly AI/ML services
Strong Senior Product Manager — Vertical Products (Fintech, Growth & Conversion) Profile
2
Mandatory(Experience 1)- Must have 7+ years of product management experience, with the most recent 3 years spent owning a consumer-facing product surface (app, web funnel, onboarding, checkout, or equivalent B2C surface
3
Mandatory(Experience 2)- Must have a metric-backed track record improving numbers — owned numbers, a steady experimentation cadence (A/B tests, rollouts, readouts), and comfort with analytics: funnels, cohorts, drivers, causal reasoning, measurement hygiene
4
Mandatory(Experience 3)- Must be fluent with AI — active, habitual use of AI tools (ChatGPT, Claude, Gemini, Perplexity, or equivalent) in daily product practice
5
Mandatory(Stakeholder Mgmt)- Must be able to act as the primary link between business leaders, tech teams, design teams and external partners — communicating roadmap, priorities, trade-offs, and performance readouts to senior stakeholders
6
Mandatory(Product Writing & Agile Delivery) - Must have strong product writing — problem briefs, PRD-lite/user stories, experiment plans, decision logs — and deep working command of Agile frameworks and tools
7
Mandatory(Company) - Must be from Top B2C product company (FinTech , InsuranceTech is the priority)
8
Mandatory (City) - Pune Based Candidates Only
9
Mandatory ( Note ) - The 1st round will be in person, so we need candidates who are available to attend the interview physically.
Strong Senior Product Manager — Vertical Products (Fintech, Growth & Conversion) Profile
2
Mandatory(Experience 1)- Must have 7+ years of product management experience, with the most recent 3 years spent owning a consumer-facing product surface (app, web funnel, onboarding, checkout, or equivalent B2C surface
3
Mandatory(Experience 2)- Must have a metric-backed track record improving numbers — owned numbers, a steady experimentation cadence (A/B tests, rollouts, readouts), and comfort with analytics: funnels, cohorts, drivers, causal reasoning, measurement hygiene
4
Mandatory(Experience 3)- Must be fluent with AI — active, habitual use of AI tools (ChatGPT, Claude, Gemini, Perplexity, or equivalent) in daily product practice
5
Mandatory(Stakeholder Mgmt)- Must be able to act as the primary link between business leaders, tech teams, design teams and external partners — communicating roadmap, priorities, trade-offs, and performance readouts to senior stakeholders
6
Mandatory(Product Writing & Agile Delivery) - Must have strong product writing — problem briefs, PRD-lite/user stories, experiment plans, decision logs — and deep working command of Agile frameworks and tools
7
Mandatory(Company) - Must be from Top B2C product company (FinTech , InsuranceTech is the priority)
8
Mandatory (City) - Pune Based Candidates Only
9
Mandatory ( Note ) - The 1st round will be in person, so we need candidates who are available to attend the interview physically.
Design and develop Agentic AI systems using LLMs, tools, memory,
workflows, and MCP.
Build production-grade RAG pipelines, including ingestion, chunking,
embeddings, retrieval, reranking, and evaluation.
Implement context engineering strategies for improving LLM accuracy,
relevance, and reliability.
Develop and integrate MCP-based tools and services for AI agents.
Work with LLMs, SLMs, quantized models, and model optimization
techniques for efficient inference.
Develop scalable backend services and APIs for AI applications.
Design databases and data models supporting AI/agentic applications.
Implement AI observability covering latency, token usage, cost, failures,
quality, and agent/tool execution.
Apply AI governance and responsible AI practices, including security,
access control, data privacy, and auditability.
Optimize AI systems for latency, scalability, cost, and reliability.
Collaborate with engineering and product teams to take AI solutions from
POC to production.
Strong hands-on experience with GenAI, LLMs, and Agentic AI.
Experience building RAG applications.
Strong understanding of Context Engineering and prompt/context
optimization.
Role Overview
We are looking for a hands-on AI/ML Engineer to design, develop, and deploy
production-ready GenAI and Agentic AI applications. The role involves building
intelligent agents, RAG pipelines, AI APIs, backend services, and scalable AI
infrastructure with a strong focus on context engineering, observability,
governance, and model optimisation.
Key Responsibilities
Required Skills
Practical experience with MCP (Model Context Protocol).
Experience with frameworks such as LangChain, LangGraph,
LlamaIndex, or equivalent.
Knowledge of LLM/SLM deployment and quantization techniques.
Strong Python backend development experience.
Experience developing REST APIs using FastAPI/Flask or equivalent.
Strong understanding of SQL/NoSQL databases and database design.
Experience with vector databases such as Qdrant, Pinecone, Weaviate,
ChromaDB, or FAISS.
Understanding of AI observability, evaluation, monitoring, and
governance.
Experience with cloud platforms and production deployment is preferred.
Strong understanding of software engineering principles, Git, testing, and
CI/CD.
Roles & Responsibilities
Product Strategy & Discovery
- Identify customer, business, and market opportunities through stakeholder engagement, customer research, discovery workshops, and data analysis.
- Define product vision, roadmap, value proposition, success metrics, and prioritization aligned with business objectives.
- Translate business and customer needs into product requirements, user stories, PRDs, and acceptance criteria.
- Own and continuously prioritize the product backlog based on business value, feasibility, dependencies, risks, and strategic priorities.
AI & Digital Innovation
- Identify and evaluate opportunities to leverage Generative AI, automation, and emerging technologies across products and business processes.
- Define AI-focused use cases, business cases, success metrics, user journeys, and MVP/PoC strategies.
- Collaborate with AI/ML, data, architecture, and engineering teams to convert AI opportunities into scalable product solutions.
- Drive adoption of AI across the product lifecycle and SDLC, including discovery, requirements management, development, testing, analytics, documentation, and support.
- Contribute to enterprise-wide AI initiatives and help scale successful AI PoCs into production-ready solutions.
Presales, Consulting & Solutioning
- Lead or contribute to RFP responses, proposals, client discovery sessions, workshops, and solution presentations.
- Understand client challenges and translate them into digital transformation, modernization, and AI-driven product opportunities.
- Develop solution concepts, use cases, product capabilities, roadmaps, value propositions, and implementation strategies.
- Partner with engineering, UX, architecture, delivery, and commercial teams to define scope, estimates, assumptions, dependencies, risks, and solution feasibility.
- Support client engagements and identify opportunities for account growth through product enhancements, modernization initiatives, and AI adoption.
Product Delivery & Governance
- Partner with UX, engineering, QA, architecture, and delivery teams to ensure successful product execution and delivery.
- Facilitate sprint planning, backlog refinement, UAT/SIT activities, release planning, production validation, and defect prioritization.
- Monitor product KPIs, delivery metrics, adoption, business outcomes, risks, and dependencies.
- Drive stakeholder alignment and continuous improvement throughout the product lifecycle.
- Mentor and guide Product Managers and product team members to strengthen product thinking, decision-making, stakeholder management, and delivery excellence.
Customer & Business Outcomes
- Leverage customer feedback, product analytics, experimentation, and market insights to drive continuous product improvement.
- Lead adoption and successful rollout of new capabilities through effective stakeholder engagement, communication, and change management.
- Ensure products and solutions deliver measurable customer, business, and operational outcomes.
Core Competencies
- Product strategy, discovery, roadmap planning, prioritization, and backlog management.
- Generative AI, AI product thinking, AI use-case identification, and digital innovation.
- Presales, consulting, solutioning, RFP response management, and client engagement.
- Strong business acumen with the ability to connect technology initiatives to business outcomes.
- Excellent communication, presentation, facilitation, and stakeholder management skills.
- Strong understanding of Agile/Scrum methodologies and end-to-end product delivery.
- Ability to collaborate effectively with engineering, architecture, UX, QA, data, and AI/ML teams.
- Strong analytical, problem-solving, and decision-making capabilities.
- Ability to manage ambiguity, multiple workstreams, and senior client stakeholders.
Qualifications
- B.E./B.Tech in Computer Science, Information Technology, Engineering, or an equivalent discipline.
- MBA or certifications in Product Management, Business Analysis, or related fields preferred.
- 10+ years of experience in Product Management, Product Ownership, or Business Analysis.
- Experience working in Agile/Scrum environments with cross-functional technology teams.
- CSPO, PSPO, CBAP, or CCBA certifications are desirable.
Location - India (Remote)
Strong Senior Product Manager — Vertical Products (Fintech, Growth & Conversion) Profile
2
Mandatory(Experience 1)- Must have 7+ years of product management experience, with the most recent 3 years spent owning a consumer-facing product surface (app, web funnel, onboarding, checkout, or equivalent B2C surface
3
Mandatory(Experience 2)- Must have a metric-backed track record improving numbers — owned numbers, a steady experimentation cadence (A/B tests, rollouts, readouts), and comfort with analytics: funnels, cohorts, drivers, causal reasoning, measurement hygiene
4
Mandatory(Experience 3)- Must be fluent with AI — active, habitual use of AI tools (ChatGPT, Claude, Gemini, Perplexity, or equivalent) in daily product practice
5
Mandatory(Stakeholder Mgmt)- Must be able to act as the primary link between business leaders, tech teams, design teams and external partners — communicating roadmap, priorities, trade-offs, and performance readouts to senior stakeholders
6
Mandatory(Product Writing & Agile Delivery) - Must have strong product writing — problem briefs, PRD-lite/user stories, experiment plans, decision logs — and deep working command of Agile frameworks and tools
7
Mandatory(Company) - Must be from Top B2C product company (FinTech , InsuranceTech is the priority)
Senior Agentic AI Engineer - (Freelance)
Positions: 2
Experience: Ideally 4(J–(J8 years with strong software-engineering fundamentals and recent hands-on Agentic AI experience.
Mission
Build UC2's governed AI agents capable of reasoning across and interacting safely with enterprise IT systems.
Mandatory capabilities
- Python
- LangGraph
- Agentic AI
- Tool/function calling
- Stateful workflows
- Structured outputs
- Human-in-the-loop
- Guardrails
- Agent state/checkpointing
- Agent evaluation
- FastAPI
- REST APIs
- Async Python
Retry/timeout/error handling
Highly desirable
MCP, LangChain, Semantic Kernel, agent observability, event-driven architecture and experience integrating AI agents with ServiceNow/Splunk/Confluence or similar enterprise platforms.
The candidate should understand how to engineer:
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Full Stack Developer - Averlon