Urgent opening for Sr. AI Engineer

Job Summary

We are looking for an experienced AI/ML Engineer to design, develop, deploy, and maintain machine learning and AI solutions that address complex business problems. The ideal candidate should have strong hands-on experience in Python, Machine Learning, Generative AI, LLMs, and AI/ML deployment, with the ability to work across the complete AI/ML lifecycle.

Key Responsibilities

• Design, develop, and deploy scalable Machine Learning and AI models for real-world business use cases.
• Build and optimize ML pipelines covering data preparation, feature engineering, model development, evaluation, and deployment.
• Develop solutions using Generative AI, Large Language Models (LLMs), NLP, and deep learning.
• Work with LLMs, prompt engineering, embeddings, vector databases, and Retrieval-Augmented Generation (RAG) architectures.
• Integrate AI/ML models with enterprise applications and APIs.
• Fine-tune and evaluate ML/LLM models based on business requirements.
• Implement MLOps practices for model versioning, deployment, monitoring, and continuous improvement.
• Collaborate with Data Scientists, Software Engineers, Architects, Product Managers, and business stakeholders.
• Conduct model performance evaluation, optimization, and troubleshooting.
• Ensure AI solutions meet requirements around security, scalability, reliability, responsible AI, and data privacy.
• Stay current with emerging AI/ML technologies, frameworks, and industry best practices.

Required Skills

• Strong programming experience in Python.
• Strong understanding of Machine Learning algorithms, statistics, and data structures.
• Hands-on experience with ML frameworks such as TensorFlow, PyTorch, Scikit-learn, or equivalent.
• Experience with Generative AI and LLMs such as OpenAI, Azure OpenAI, Claude, Gemini, or open-source models.
• Strong knowledge of Prompt Engineering, RAG, embeddings, vector databases, and AI agents.
• Experience with NLP, deep learning, or computer vision is an advantage.
• Experience developing and consuming REST APIs and microservices.
• Working knowledge of SQL and NoSQL databases.
• Experience with cloud platforms such as Azure, AWS, or GCP.
• Understanding of Docker, Kubernetes, CI/CD, and MLOps.
• Familiarity with Git and modern software development practices.

Preferred Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.
• 4 years of relevant experience in AI/ML engineering or a related field.
• Experience building and deploying production-grade AI/ML solutions.
• Enterprise application development experience.
• Experience with Azure OpenAI, AWS Bedrock, Vertex AI, or similar managed AI platforms.
• Experience with LangChain, LlamaIndex, Semantic Kernel, or comparable AI frameworks is a plus.
• Experience with AI/ML model monitoring, evaluation, and optimization.

What You Bring

• Strong problem-solving and analytical skills.
• Ability to translate business requirements into practical AI/ML solutions.
• Strong software engineering and debugging capabilities.
• Ability to work independently as well as collaboratively in a cross-functional environment.
• Good communication skills with the ability to explain complex AI concepts to technical and non-technical stakeholders.

Keywords

AI Engineer | ML Engineer | Machine Learning | Generative AI | LLM | Python | NLP | Deep Learning | RAG | Prompt Engineering | AI Agents | Azure OpenAI | AWS Bedrock | MLOps | TensorFlow | PyTorch | Scikit-learn | Vector Database | Cloud AI

Location: Gurugram

Work mode: Hybrid

Job Title: Senior AI Engineer

Experience: 5–10 years in software engineering, with 3+ years in AI/ML engineering preferred

Employment Type: Full-time

Department: Artificial Intelligence / Engineering

Location: Bangalore

We are looking for a highly skilled Senior AI Engineer to design, develop, deploy, and scale intelligent AI-powered applications and solutions. The ideal candidate will have strong expertise in machine learning, Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, and production-grade software engineering.

The candidate will work closely with product managers, data scientists, software engineers, and business stakeholders to translate complex business requirements into scalable, reliable, secure, and cost-effective AI solutions.

Key Responsibilities

1. AI Solution Design and Development

• Design, develop, and deploy end-to-end AI and machine learning solutions.
• Build intelligent applications using LLMs, Generative AI, NLP, and deep learning techniques.
• Develop scalable AI architectures and reusable components for enterprise applications.
• Evaluate AI models and select appropriate approaches based on accuracy, latency, cost, and business requirements.

2. Generative AI and LLM Engineering

• Develop LLM-powered applications using models from providers such as OpenAI, Anthropic, Google, or open-source ecosystems.
• Design and implement RAG pipelines, document processing workflows, semantic search, and knowledge retrieval systems.
• Build AI agents and multi-step workflows using appropriate orchestration frameworks.
• Implement prompt engineering, structured outputs, tool calling, and context management.
• Optimize model performance through evaluation, fine-tuning where appropriate, and inference optimization.

3. Machine Learning and Data Engineering

• Develop and operationalize machine learning models for classification, prediction, recommendation, and other business use cases.
• Work with structured, unstructured, and multimodal data.
• Build data preprocessing, feature engineering, model training, and validation pipelines.
• Apply appropriate model evaluation techniques and statistical methods.

4. Production Deployment and MLOps / LLMOps

• Deploy AI solutions using cloud platforms such as AWS, Microsoft Azure, or Google Cloud.
• Build automated CI/CD pipelines for AI applications and model deployment.
• Implement model versioning, experiment tracking, monitoring, and performance evaluation.
• Optimize inference latency, throughput, infrastructure utilization, and operating costs.
• Establish observability for AI applications, including token usage, retrieval quality, latency, failures, and model behavior.

5. Software Engineering and System Architecture

• Develop production-quality applications and APIs using Python and modern backend frameworks.
• Design RESTful APIs, asynchronous processing systems, and microservices where appropriate.
• Integrate AI services with enterprise applications, databases, and third-party systems.
• Use relational databases, vector databases, caching, and distributed systems effectively.
• Apply software engineering best practices, including testing, code reviews, documentation, and design patterns.

6. Security, Quality, and Responsible AI

• Implement safeguards against prompt injection, data leakage, insecure tool execution, and unauthorized access.
• Apply appropriate privacy, security, and compliance controls to AI systems.
• Evaluate hallucinations, bias, reliability, and failure modes.
• Design human-in-the-loop workflows and fallback mechanisms for critical use cases.

7. Technical Leadership and Collaboration

• Lead technical design discussions and mentor junior AI engineers.
• Define engineering standards, coding practices, and AI development guidelines.
• Collaborate with cross-functional teams to deliver measurable business outcomes.
• Conduct code reviews, evaluate emerging technologies, and recommend practical innovations.
• Communicate technical trade-offs and solution architecture to both technical and non-technical stakeholders.

Required Technical Skills

Programming and Backend Development

• Strong proficiency in Python.
• Experience with FastAPI, Flask, or Django.
• Knowledge of REST APIs, asynchronous programming, data structures, algorithms, and software design principles.
• Experience with Git, automated testing, and CI/CD.

AI and Machine Learning

• Strong understanding of supervised and unsupervised learning, model evaluation, and statistical concepts.
• Hands-on experience with PyTorch, TensorFlow, or scikit-learn.
• Knowledge of NLP, embeddings, transformers, and deep learning fundamentals.

Generative AI and LLMs

• Hands-on experience building production applications using LLM APIs or open-source models.
• Strong understanding of RAG architecture, embeddings, chunking, reranking, and retrieval evaluation.
• Experience with vector databases such as Pinecone, Weaviate, Qdrant, Milvus, or pgvector.
• Familiarity with orchestration frameworks such as LangChain, LangChain, or comparable tools.
• Experience with tool calling, structured generation, agent workflows, and LLM evaluation.

Cloud and Infrastructure

• Experience with AWS, Azure, or Google Cloud.
• Working knowledge of Docker and Kubernetes.
• Familiarity with model serving, scalable APIs, monitoring, and production deployments.
• Experience with MLOps or LLMOps practices.

Databases and Data Processing

• Strong SQL skills and experience with relational databases.
• Familiarity with NoSQL databases, data pipelines, and distributed processing.
• Experience working with unstructured data, document ingestion, and retrieval systems.

Preferred Qualifications

• Experience with fine-tuning, quantization, or serving open-source LLMs.
• Experience with multimodal AI, including text, image, audio, or video.
• Knowledge of agent evaluation, workflow orchestration, and multi-agent systems.
• Experience with AI security, guardrails, governance, and responsible AI.
• Experience optimizing AI workloads for latency, scalability, and cost.
• Contributions to open-source AI projects, research, or technical publications.

Education

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field.
• Equivalent practical experience and demonstrated engineering expertise may be considered.

Key Success Metrics

• Quality, accuracy, and reliability of deployed AI solutions.
• Production adoption and measurable business impact.
• Reduction in inference costs and response latency.
• RAG retrieval quality and reduction in unsupported or incorrect responses.
• System availability, scalability, and operational stability.
• Delivery timelines, code quality, and technical mentoring effectiveness.

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

🚀 WE’RE HIRING | AI/ML GENERATIVE AI ENGINEER

📍 Location: Remote

💼 Experience: 5+ Years

🔄 Position: Contract – Extendable

🔹 ROLE HIGHLIGHTS

➤ Build and deploy AI/ML amp; Generative AI solutions

➤ Develop LLM, RAG, NLP, Recommendation amp; Predictive solutions

➤ Work on AI Agents, Chatbots, Computer Vision amp; Content Intelligence

➤ Build ML models using PyTorch, TensorFlow, Keras amp; Scikit-learn

➤ Develop RAG solutions using LangChain, LlamaIndex, FAISS/Milvus

➤ Integrate OpenAI, Azure OpenAI, AWS Bedrock, Vertex AI amp; Hugging Face

➤ Build scalable AI APIs using Python, FastAPI/Flask/Django

➤ Contribute to Private AI amp; Smart Agentic Systems

⚙️ MUST-HAVE SKILLS

◆ Python – 3+ years

◆ AI/ML – 5+ years

◆ Generative AI – 2+ years

◆ LLMs, RAG, Embeddings , Transformers

◆ ML/DL, NLP amp; Predictive Analytics

◆ Cloud AI Platforms – Azure / AWS / GCP

◆ AI/ML Deployment | MLOps

Interview Process - F2F Round at Pune Location

Kody Technolab Limited is seeking an experienced AI/ML Engineer to design, develop, and deploy cutting-edge Artificial Intelligence and Machine Learning solutions. The ideal candidate will have

strong expertise in Machine Learning, Deep Learning, Generative AI, LLMs, MLOps, and cloud-based AI deployments.

Key Responsibilities

• Design, develop, and deploy Machine Learning and Deep Learning models for classification, regression, recommendation systems, NLP, Computer Vision, and Generative AI applications.

• Build and maintain end-to-end ML pipelines including data preprocessing, feature engineering, model training, validation, evaluation, and deployment.

• Develop AI solutions using PyTorch, TensorFlow, Scikit-learn, Hugging Face, and related frameworks.

• Work with Large Language Models (LLMs) and foundation models such as GPT, BERT, Llama, Claude, and Stable Diffusion.

• Collaborate with product, engineering, and business teams to translate requirements into scalable AI solutions.

• Optimize model performance, scalability, and reliability for production environments.

• Implement MLOps best practices using tools such as MLflow, Docker, Kubernetes, and Kubeflow.

• Stay updated with emerging trends and research in AI, ML, Deep Learning, and Generative AI.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, Mathematics, or a related field.

• 7+ years of hands-on experience in AI/ML product development.

• Strong proficiency in Python and ML frameworks including Scikit-learn, TensorFlow, PyTorch, and Hugging Face.

• Experience with Generative AI, LLMs, GANs, VAEs, diffusion models, and prompt engineering.

• Strong understanding of the ML lifecycle including model training, tuning, deployment, monitoring, and optimization.

• Experience with MLOps tools such as MLflow, Docker, Kubeflow, and CI/CD pipelines.

• Experience with AWS, Azure, or GCP cloud platforms.

• Strong problem-solving and analytical skills.

Preferred Skills

• Fine-tuning and deployment of Large Language Models.

• Experience with RAG (Retrieval Augmented Generation) architectures.

• Contributions to open-source AI projects or research publications.

• Knowledge of model interpretability, data annotation, and feature engineering.

• C++ experience for high-performance AI applications.

Why Join Kody Technolab Limited?

Opportunity to work on innovative AI products, Generative AI solutions, robotics integrations,

and enterprise-scale applications while collaborating with a highly skilled technology team.

Visit the Website to know more about us.

Company Website - Kody Technolab | Deep Tech Company in Robotics & AI Solution

Kody Robots | Robotics Company in India for Autonomous Robots

About the role

We are seeking an AI Engineer to build and implement AI systems for content production at scale. You'll work at the intersection of engineering and content designing prompt pipelines, integrating generative models, and building the tooling that turns source material into finished creative output. The ideal candidate is technically strong but also has taste: someone who understands story and craft, and can tell the difference between output that's technically correct and output that's actually good.

Responsibilities

• Build and iterate on prompt pipelines and multi-agent workflow components
• Design and integrate agentic workflows orchestrate multi-step, tool-using agents that plan, call models, and hand off between stages in production
• Deploy and serve open-source models set up inference endpoints, manage GPU compute, and optimize for latency and cost
• Write evals compare outputs against references, quantify quality, and feed results back into the pipeline
• Work on data pipelines: structured extraction from messy source text, localization, similarity/dedup
• Debug and maintain pipeline stages in production

What you bring:  

• (1+/3+) years of engineering experience, or a strong portfolio of shipped projects
• Solid Python fundamentals clean, working, readable code
• Hands-on experience with LLM APIs and prompt engineering (personal projects count)
• Comfort with Git, REST APIs, and working in a Linux environment
• A feel for content and narrative you can judge whether generated output is actually good, not just valid
• Curiosity and clear communication you ask good questions and don't stay stuck silently

 Preferred

• Exposure to agent/orchestration frameworks (LangGraph, LangChain, CrewAI)
• Familiarity with vector databases, embeddings, or RAG (Qdrant, pgvector)
• Hands-on work with open-source generative media models Flux, LTX, Wan, or similar
• Experience deploying open-source models for inference (vLLM, ComfyUI, Replicate/Cog, Docker + GPU)
• Experience writing evals or LLM-as-judge scoring
• Node.js and Fastapi familiarity, or experience deploying on AWS

About the role

We are seeking an AI Engineer to build and implement AI systems for content production at scale. You'll work at the intersection of engineering and content designing prompt pipelines, integrating generative models, and building the tooling that turns source material into finished creative output. The ideal candidate is technically strong but also has taste: someone who understands story and craft, and can tell the difference between output that's technically correct and output that's actually good.

Responsibilities

• Build and iterate on prompt pipelines and multi-agent workflow components
• Design and integrate agentic workflows orchestrate multi-step, tool-using agents that plan, call models, and hand off between stages in production
• Deploy and serve open-source models set up inference endpoints, manage GPU compute, and optimize for latency and cost
• Write evals compare outputs against references, quantify quality, and feed results back into the pipeline
• Work on data pipelines: structured extraction from messy source text, localization, similarity/dedup
• Debug and maintain pipeline stages in production

What you bring:  

• (1+/3+) years of engineering experience, or a strong portfolio of shipped projects
• Solid Python fundamentals clean, working, readable code
• Hands-on experience with LLM APIs and prompt engineering (personal projects count)
• Comfort with Git, REST APIs, and working in a Linux environment
• A feel for content and narrative you can judge whether generated output is actually good, not just valid
• Curiosity and clear communication you ask good questions and don't stay stuck silently

 Preferred

• Exposure to agent/orchestration frameworks (LangGraph, LangChain, CrewAI)
• Familiarity with vector databases, embeddings, or RAG (Qdrant, pgvector)
• Hands-on work with open-source generative media models Flux, LTX, Wan, or similar
• Experience deploying open-source models for inference (vLLM, ComfyUI, Replicate/Cog, Docker + GPU)
• Experience writing evals or LLM-as-judge scoring
• Node.js and Fastapi familiarity, or experience deploying on AWS

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

Immediate Joiner - 15 days Notice period

• Primary Skillsets: Python, Generative AI, Agentic AI, LLM, experience with designing and developing production grade AI solutions, AWS/Azure/GCP, LLMOps, Data Engineering (Databricks/Snowflake), Regulated GenAI, Data and Decisions, and Agentic Systems.

• Preferred: Pharma or life sciences experience, especially in regulated (GxP) environments, experience setting up a team or practice from scratch.

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.