Lead AI Engineer WFH

Strong Senior AI Engineer Profile with production-grade LLM and Python expertise

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Mandatory (Experience 1): Must have at least 3+ years of professional AI/ML engineering experience with demonstrated track record of delivering production-grade AI systems in real-world environments

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Mandatory (Tech skill 1): Must have strong programming skills in Python and SQL, with hands-on ML/data libraries (scikit-learn, pandas, numpy) and deep-learning frameworks (PyTorch or TensorFlow), plus REST API design

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Mandatory (Experience 2): Must have hands-on experience building and deploying production-grade ML/LLMs including RAG pipelines, document parsing, information extraction, and text processing on large-scale unstructured data (preprocessing, chunking, embeddings, feature engineering)

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Mandatory (Tech skill 2): Must have strong NLP / extraction-focused ML depth — transformers, embeddings, vector databases, RAG, LLM integrations, and agentic workflows.

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Mandatory (Tech skill 3): Must have hands-on experience with AWS (SageMaker, Bedrock, EC2, Lambda) and Azure (AI Foundry, Azure OpenAI) for model training, fine-tuning, deployment, and inference at scale

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Mandatory (Tech skill 4): Must have experience with multi-agentic frameworks / orchestration tools (Claude Code, LangGraph, LangChain, CrewAI)

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Mandatory (Tech skill 5): Must have hands-on experience with MLOps ecosystem including experiment tracking (MLflow, Weights & Biases), model versioning/registry, automated retraining, and CI/CD (GitHub Actions, Azure DevOps, Docker, Kubernetes)

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Mandatory (Tech skill 6): Must have experience with evaluation frameworks (precision, recall, F1, field-level accuracy) and iterative model improvement, plus AI observability (Prometheus, Grafana, SLOs).

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Mandatory (Team Leadership): Must have experience leading projects or teams, managing technical deliverables, and mentoring, with strong problem-solving in ambiguous challenges

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Mandatory (Education) - B.Tech or B.E. If graduated in BCA or BCS, they should have a Maters or Post graduation

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Mandatory (Company): B2B Tech/Consulting/B2B Software

Strong Senior AI Engineer Profile with production-grade LLM and Python expertise

2

Mandatory (Experience 1): Must have at least 3+ years of professional AI/ML engineering experience with demonstrated track record of delivering production-grade AI systems in real-world environments

3

Mandatory (Tech skill 1): Must have strong programming skills in Python and SQL, with hands-on ML/data libraries (scikit-learn, pandas, numpy) and deep-learning frameworks (PyTorch or TensorFlow), plus REST API design

4

Mandatory (Experience 2): Must have hands-on experience building and deploying production-grade ML/LLMs including RAG pipelines, document parsing, information extraction, and text processing on large-scale unstructured data (preprocessing, chunking, embeddings, feature engineering)

5

Mandatory (Tech skill 2): Must have strong NLP / extraction-focused ML depth — transformers, embeddings, vector databases, RAG, LLM integrations, and agentic workflows.

6

Mandatory (Tech skill 3): Must have hands-on experience with AWS (SageMaker, Bedrock, EC2, Lambda) and Azure (AI Foundry, Azure OpenAI) for model training, fine-tuning, deployment, and inference at scale

7

Mandatory (Tech skill 4): Must have experience with multi-agentic frameworks / orchestration tools (Claude Code, LangGraph, LangChain, CrewAI)

8

Mandatory (Tech skill 5): Must have hands-on experience with MLOps ecosystem including experiment tracking (MLflow, Weights & Biases), model versioning/registry, automated retraining, and CI/CD (GitHub Actions, Azure DevOps, Docker, Kubernetes)

9

Mandatory (Tech skill 6): Must have experience with evaluation frameworks (precision, recall, F1, field-level accuracy) and iterative model improvement, plus AI observability (Prometheus, Grafana, SLOs).

10

Mandatory (Team Leadership): Must have experience leading projects or teams, managing technical deliverables, and mentoring, with strong problem-solving in ambiguous challenges

11

Mandatory (Education) - B.Tech or B.E. If graduated in BCA or BCS, they should have a Maters or Post graduation

12

Mandatory (Company): B2B Tech/Consulting/B2B Software

Applied AI Engineer

About the Role

We are building an AI-powered platform that transforms credit origination by automating manual processes and enabling lenders to underwrite more loans with greater accuracy and lower risk.

We are looking for an experienced Applied AI Engineer to design and build production-grade Agentic AI systems capable of complex, multi-step reasoning across financial data, legal documents, and other unstructured information.

As an early engineering team member, you will take ownership of core AI architecture, multi-agent orchestration, reasoning workflows, evaluation frameworks, and scalable AI infrastructure.

Key Responsibilities

• Design and develop stateful, multi-agent AI systems for complex reasoning and decision-making.
• Build and optimize LLM pipelines using frameworks such as LangChain, DSPy, and GEPA.
• Implement RAG, prompt optimization, memory management, tool integration, and human-in-the-loop workflows.
• Develop AI evaluation frameworks using LLM-as-a-Judge and scenario-based testing.
• Implement AI observability and monitoring to track accuracy, latency, cost, and system performance.
• Build ETL pipelines and work with structured and unstructured data, SQL/NoSQL databases, vector databases, and graph-based data models.
• Integrate external tools and systems using modern agent protocols such as Model Context Protocol (MCP).
• Deploy and maintain AI services on AWS or Azure.
• Implement CI/CD pipelines, containerization, and Infrastructure as Code for reliable production deployments.

Required Qualifications

• 5+ years of professional software development experience in Python or JavaScript.
• Proven experience building and deploying production-level Agentic AI systems or complex LLM reasoning applications.
• Hands-on experience with LangChain and LLMOps, including tools such as Langfuse, Weights & Biases (W&B), or Helicone.
• Strong understanding of LLMs, RAG, prompt engineering, multi-agent orchestration, memory management, and tool calling.
• Experience with DSPy, GEPA, or advanced LLM inference and optimization techniques.
• Strong data engineering fundamentals, including ETL, SQL/NoSQL databases, and vector databases.
• Familiarity with MCP and human-in-the-loop workflows.
• Experience with AWS or Azure and modern DevOps practices, including Docker, CI/CD, and Infrastructure as Code.

Good to Have

• Experience with RLHF or RLAIF.
• Knowledge of model fine-tuning techniques such as LoRA and QLoRA.
• Experience with graph databases and graph-based data models.
• Exposure to financial services, credit underwriting, risk analysis, or document-intensive AI applications.

Why Join Us?

• Work on advanced Agentic AI and complex reasoning systems beyond conventional chatbot applications.
• Build foundational AI architecture and production-grade LLM workflows.
• Take ownership of technical decisions and system design as an early engineering team member.
• Solve challenging problems at the intersection of AI, finance, and intelligent automation.

Role: Python + Agentic AI Engineer

We are looking for an experienced Python + Agentic AI Engineer with strong expertise in developing AI-powered applications and autonomous agent-based solutions.

Key Skills / Requirements:

• Strong hands-on experience in Python

• Experience with Agentic AI / AI Agents

• Hands-on with LangChain / LangGraph or similar agent frameworks

• Experience with Generative AI and LLMs

• Strong understanding of RAG (Retrieval-Augmented Generation) and Vector Databases

• Experience developing REST APIs using FastAPI

• Knowledge of Multi-Agent Systems, Tool/Function Calling and Agent Workflows

• Experience integrating LLMs with enterprise applications/APIs

• Exposure to cloud-based AI services is an advantage

Preferred Profile: Python Developer / AI Engineer / Generative AI Engineer / Agentic AI Engineer with hands-on experience building production-ready AI solutions.

Role Overview

We are looking for an experienced AI/ML Engineer with strong expertise in Python, Generative AI, LLMs, LangChain, and LangGraph. The candidate will be responsible for designing and developing AI-powered applications, intelligent agents, and scalable LLM-based solutions.

Key Responsibilities

• Design and develop AI/ML and Generative AI applications using Python and modern LLM technologies.
• Build LLM-based applications and AI agents using LangChain and LangGraph.
• Develop agentic workflows involving tool calling, memory, reasoning, and multi-step orchestration.
• Integrate LLMs such as OpenAI, Azure OpenAI, Anthropic, Gemini, or other foundation models.
• Develop RAG (Retrieval-Augmented Generation) pipelines using vector databases.
• Work with embeddings, prompt engineering, semantic search, and document processing.
• Develop scalable APIs and backend services using Python, FastAPI, or Flask.
• Build and integrate AI solutions with existing applications and enterprise systems.
• Deploy and maintain AI/ML solutions on cloud platforms.
• Collaborate with data scientists, software engineers, and product teams to develop business-focused AI solutions.

Required Skills

• Strong hands-on experience in Python programming.
• Strong experience in AI/ML and Generative AI.
• Hands-on experience with LLMs and LLM-based application development.
• Strong experience with LangChain and/or LangGraph.
• Experience building AI Agents / Agentic AI workflows.
• Strong understanding of RAG, embeddings, vector databases, and prompt engineering.
• Experience with vector databases such as FAISS, Pinecone, Chroma, Weaviate, or Azure AI Search.
• Experience developing REST APIs using FastAPI/Flask.
• Good understanding of Machine Learning, NLP, and deep learning concepts.
• Experience with Azure / AWS / GCP cloud platforms.

Good to Have

• Experience with multi-agent systems and agent orchestration.
• Knowledge of MLOps / LLMOps.
• Experience with Docker, Kubernetes, and CI/CD.
• Knowledge of LLM evaluation, monitoring, observability, and AI governance.
• Experience with Azure OpenAI, Azure AI Foundry, or AWS Bedrock.

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

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Design and build LLM-powered agentic systems using frameworks such as LangChain, LlamaIndex, AutoGen, or CrewAI to automate complex, multi-step workflows.

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Develop and maintain Retrieval-Augmented Generation (RAG) pipelines with vector databases (Pinecone, Weaviate, Chroma, pgvector) for domain-specific knowledge grounding.

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Build and integrate tool-use and function-calling capabilities into AI agents, enabling dynamic interaction with internal APIs, databases, and third-party services.

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Implement prompt engineering strategies including chain-of-thought, few-shot prompting, and structured output parsing to ensure reliable agent behavior.

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Design evaluation frameworks and observability pipelines (LangSmith, Helicone, custom metrics) to monitor agent performance, accuracy, and cost.

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Collaborate with product, sales, and domain teams to translate business requirements into AI-driven solutions and features.

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Optimize LLM inference for latency and cost using techniques like caching, model distillation, quantization, and batching.

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Stay current with the rapidly evolving LLM ecosystem and proactively propose improvements and new approaches.

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Contribute to internal best practices, documentation, and knowledge-sharing across the engineering org.

Required Qualifications

Experience

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2–4 years of professional software engineering experience, with at least 1–2 years focused on LLM/AI systems.

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Proven experience shipping LLM-based products or agentic AI systems into production environments.

Technical Skills

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Strong proficiency in Python and familiarity with async programming patterns for AI pipelines.

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Hands-on experience with LLM APIs: OpenAI (GPT-4o), Anthropic (Claude), Google (Gemini), or open-source models (Llama, Mistral).

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Experience with agentic frameworks: LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI, or similar.

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Solid understanding of RAG architectures, embedding models, and semantic search.

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Experience with vector databases and similarity search infrastructure.

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Knowledge of REST APIs, microservices architecture, and containerization (Docker/Kubernetes).

Problem-Solving & Mindset

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Strong ability to decompose ambiguous, open-ended problems into structured AI system designs.

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Experience with prompt debugging, LLM evaluation, and iterative refinement workflows.

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Ability to balance research exploration with engineering pragmatism to ship reliable systems.

Preferred Qualifications

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Experience with multi-agent orchestration and agent memory systems (short-term and long-term).

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Familiarity with fine-tuning or RLHF workflows for domain adaptation.

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Background in NLP, information retrieval, or conversational AI.

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Prior experience in B2B SaaS or CRM domain is a plus.

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Contributions to open-source AI/ML projects or published research/blogs.

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Experience with cloud platforms: AWS, GCP, or Azure — particularly AI/ML services

🚨 Hiring – Data Scientist | Python + Agentic AI

💼 Experience: 5+ Years

Must Have:

• Strong Data Science experience

• Python

• Agentic AI / AI Agents

• Generative AI / LLMs

• RAG / Vector Databases

• LangChain / LangGraph or similar Agent Frameworks

• Machine Learning & NLP

Strong AI Engineer / Machine Learning Engineer profiles.

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Mandatory (Experience 1) – Must have minimum 3+ years of hands-on experience in Data Science, Machine Learning, Applied AI, NLP, Deep Learning, or Generative AI solutions.

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Mandatory (Experience 2) – Must have strong hands-on experience in Python programming, SQL, data analysis, feature engineering, model development, and production-grade ML applications.

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Mandatory (Experience 3) – Must have experience working with Machine Learning and Deep Learning frameworks such as PyTorch, TensorFlow, Keras, Scikit-learn, or equivalent.

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Mandatory (Experience 4) – Must have hands-on experience working on NLP, embeddings, semantic search, text classification, document understanding, recommendation systems, or similar AI/ML use cases.

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Mandatory (Experience 5) – Must have experience working with Large Language Models (LLMs) such as GPT, Llama, Mistral, Claude, Gemini, Phi, or similar foundation models.

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Mandatory (Experience 6) – Must have hands-on experience building or implementing RAG (Retrieval Augmented Generation) systems, vector search, knowledge retrieval, embeddings, chunking, indexing, or semantic retrieval solutions.

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Mandatory (Experience 7) – Must have experience working with Git, CI/CD practices, production environments, and scalable AI/ML systems.

Mandatory ( Pedigree) - B.TECH / M.TECH from Tier 1 Colleges (IIT's, NIT's, BITS) are Considered.

AI Engineer

Focus: Agentic AI, AI Shopping Assistants & Virtual Try-On

Role Summary :

We are looking for a Full-Stack AI Engineer to build, deploy, and operate production-grade AI applications, including agentic shopping assistants and multimodal AI experiences. The role requires strong hands-on expertise in Python, TypeScript, LLM-based applications, real-time streaming, evaluation, and cloud infrastructure.

The ideal candidate can independently deliver end-to-end features across backend agent runtimes and frontend interfaces, with a strong focus on reliability, latency, cost optimisation, and production performance.

Key Responsibilities :

• AI Agent Development: Build and maintain Python-based agent runtimes using LLMs, tool calling, structured outputs, retrieval-augmented generation (RAG), multi-turn state, and human escalation workflows.
• Full-Stack Engineering: Develop asynchronous Python APIs and TypeScript web components, implementing server-sent events (SSE), streaming interfaces, interactive product cards, and resilient client-side error handling.
• LLM Evaluation: Build evaluation datasets and automated test harnesses using deterministic and model-based graders, regression gates, ablation studies, and pass@k/pass^k metrics.
• Performance & Cost Optimisation: Improve response latency, time to first token, token consumption, caching efficiency, context management, and cost per conversation.
• Production Operations: Implement distributed tracing, structured logging, LLM observability, monitoring, incident resolution, feature flags, controlled rollouts, and rollback mechanisms.
• Cloud & DevOps: Deploy containerised services, manage CI/CD pipelines, infrastructure as code, and production workloads on cloud platforms.
• E-commerce AI Integration: Integrate product search, catalogue data, product details, grounded recommendations, and commerce-related event tracking into AI-powered shopping experiences.
• Virtual Try-On & Multimodal AI: Contribute to image-based generative AI applications, including embeddable frontend widgets and scalable backend services.
• Internationalisation & Scalability: Support multilingual experiences, brand-specific configurations, localisation, currency handling, and backward-compatible integrations.
• Cross-Functional Collaboration: Work with product, business, and engineering stakeholders to troubleshoot quality issues and deliver measurable improvements to customer experience and business outcomes.

Essential Skills & Experience :

• Strong hands-on experience with Python and TypeScript.
• Production experience building LLM-powered applications using tool/function calling, structured outputs, streaming, retries, context management, and RAG.
• Strong understanding of asynchronous Python, REST APIs, SSE, and event-driven communication.
• Practical experience designing LLM evaluation frameworks, test datasets, automated graders, regression testing, and evaluation metrics.
• Experience deploying and operating cloud-based applications, containerised services, CI/CD pipelines, and infrastructure as code.
• Familiarity with production observability, distributed tracing, structured logging, performance monitoring, and incident management.
• Experience with caching, latency optimisation, token and inference cost management, feature flags, and safe deployment strategies.
• Strong software engineering fundamentals, debugging skills, and end-to-end ownership of production features.

Preferred Skills :

• Experience with agent frameworks such as Google ADK, LangGraph, or Claude Agent SDK.
• Hands-on exposure to Google Cloud Platform, Vertex AI, Gemini, FastAPI, and Cloud Run.
• Experience with frontend frameworks and tools such as Lit, Next.js, esbuild, and Vitest.
• Familiarity with PostgreSQL, BigQuery, OpenTelemetry, Opik, and LLM tracing platforms.
• Knowledge of MCP servers, sub-agents, agent-to-agent integrations, and retrieval systems.
• Experience with e-commerce search, multimodal AI, image generation, virtual try-on, and third-party web embeds.
• Understanding of prompt-injection mitigation, multilingual applications, and inference performance optimisation.
• Effective use of AI coding assistants and developer productivity tools.

Role Expectations :

• Deliver production-ready features across both backend AI services and frontend applications.
• Demonstrate evaluation evidence, observability, and safe rollout practices for AI-driven changes.
• Balance response quality, reliability, latency, infrastructure costs, and user experience.
• Work effectively within existing systems, maintaining backward compatibility and planning migrations and rollbacks.
• Take ownership of troubleshooting, continuous improvement, and production reliability.

Hiring AI Engineer

Exp : 5 - 8 yrs

Edu : BE/B.Tech/MCA

Work Location : Pune WFO

Skills :

Min 4 years strong programming experience in Python is a MUST.

Min 2 years hands-on experience in AI with LLMs, RAG pipelines, and AI frameworks

RAG with deep retrieval experience (most important):

Owned a RAG system end to end in production, not a demo.

Has built an MCP server or client, or built agents using LangGraph/Semantic Kernel (tool calling, multi-step workflows).