Generative AI Developer

Job Summary:

Wissen Technology is hiring an AI Implementation Engineer to build, deploy, and scale enterprise-grade Generative AI solutions across business-critical applications. The role involves developing production-ready AI systems using Azure AI services, Large Language Models (LLMs), RAG architectures, and agent-based frameworks while collaborating closely with engineering teams to drive AI adoption and innovation.

Experience

6-12 years

Location

Mumbai / Bangalore

Mode of Work

Hybrid

Mandatory Skills (Must Have)

• Python programming (6+ years) including asynchronous programming and backend application development

• Java and Spring Framework (3+ years) for enterprise-scale application integration

• Azure OpenAI Service, Azure AI Foundry, and Azure AI Search for production GenAI applications

• Retrieval Augmented Generation (RAG) architecture including embeddings, chunking, vector databases, reranking, and grounding techniques

• Agent Frameworks such as Microsoft Agent Framework, Semantic Kernel, AutoGen, LangChain, or LangGraph

• Snowflake and Cortex AI (Cortex Search, LLM Functions) with strong SQL expertise

• Prompt Engineering, LLM evaluation frameworks, testing, and model performance optimization

• DevOps and Cloud Deployment using Azure DevOps, GitHub Actions, Docker, AKS, Azure Functions, and observability tools

Optional Skills (Good to Have)

• React.js for AI-powered user interfaces and conversational applications

• Azure AI Content Safety and Responsible AI implementation experience

• Financial Services, Banking, or other regulated industry domain experience

• Real-time streaming applications and token-level LLM operations

• Performance optimization, caching strategies, and cost optimization for AI workloads

• Microsoft Azure AI Engineer Associate Certification

Senior Generative AI Engineer

Employment Type: Permanent with VDart Digital

Work Location: Marathalli, Bengaluru

Job Description

We are seeking a highly skilled Senior Generative AI Engineer with strong expertise in designing, developing, and deploying enterprise-scale AI solutions using Large Language Models (LLMs) and modern Generative AI frameworks. The ideal candidate should have hands-on production experience building scalable GenAI applications, AI agents, autonomous workflows, and Retrieval-Augmented Generation (RAG) systems in cloud-native environments.

This role requires deep technical expertise in LLM orchestration, AI application architecture, prompt engineering, vector databases, MLOps, and production deployment of AI systems. Candidates should have proven experience delivering real-world AI solutions in enterprise environments with strong exposure to cloud platforms and DevOps practices.

Key Responsibilities

• Design, build, and deploy enterprise-grade Generative AI applications using Large Language Models (LLMs).
• Develop intelligent AI agents and autonomous workflows using frameworks such as LangChain, CrewAI, LangGraph, AutoGen, or similar agentic AI frameworks.
• Implement and optimize Retrieval-Augmented Generation (RAG) pipelines using vector databases and semantic search technologies.
• Work extensively on prompt engineering, tool calling, memory management, agent orchestration, and multi-agent systems.
• Integrate and manage LLMs such as OpenAI, Azure OpenAI, Claude, Gemini, Llama, Mistral, or similar foundation models.
• Develop scalable AI services and APIs using Python and FastAPI.
• Build production-ready AI solutions with high availability, scalability, monitoring, and observability.
• Deploy and manage AI applications in cloud-native environments using Docker and Kubernetes.
• Collaborate with Data Science, ML Engineering, and DevOps teams to operationalize AI solutions.
• Implement CI/CD pipelines and automated deployment processes for AI workloads.
• Monitor model performance, latency, reliability, and operational efficiency in production environments.
• Ensure AI solutions follow enterprise security, governance, and responsible AI standards.
• Evaluate and adopt emerging Generative AI tools, frameworks, and models.

Required Skills

Generative AI & LLM Expertise

• Strong hands-on experience with Generative AI and Large Language Models (LLMs).
• Production-level experience building and deploying GenAI applications.
• Expertise in LangChain, CrewAI, LangGraph, AutoGen, or similar frameworks.
• Experience with AI agents, autonomous workflows, and multi-agent architectures.
• Strong understanding of prompt engineering, embeddings, model evaluation, and LLM orchestration.
• Experience integrating OpenAI, Azure OpenAI, Claude, Gemini, Llama, Mistral, or similar models.

RAG & Vector Databases

• Strong experience implementing RAG pipelines and semantic retrieval systems.
• Experience with vector databases such as Pinecone, Weaviate, ChromaDB, FAISS, or Milvus.
• Understanding of chunking strategies, embeddings, indexing, reranking, and retrieval optimization.

Python & AI Development

• Strong proficiency in Python.
• Experience with FastAPI for AI service and API development.
• Experience with AI/ML libraries and data processing tools such as Pandas and NumPy.

Cloud & Production Deployment

• Mandatory production experience on at least one cloud platform:
• Microsoft Azure
• Experience deploying scalable AI applications in enterprise production environments.
• Hands-on experience with Docker, Kubernetes, Jenkins, Terraform, and CI/CD pipelines.
• Strong understanding of MLOps, AI deployment lifecycle, monitoring, and observability.

Engineering & Operational Excellence

• Strong understanding of software engineering best practices.
• Experience with Git, version control, automated testing, and release management.
• Experience building secure, scalable, and high-performance AI solutions.
• Ability to troubleshoot production AI systems and optimize performance.

Preferred Skills

• Experience with AI observability and evaluation frameworks.
• Exposure to fine-tuning, PEFT, LoRA, or model optimization techniques.
• Experience with enterprise AI governance and responsible AI practices.
• Knowledge of distributed AI systems and scalable inference architectures.
• Familiarity with AI security and compliance standards.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
• 3–8 years of overall software engineering experience.
• Minimum 3+ years of hands-on experience in Generative AI and LLM-based application development,
• Proven track record of delivering enterprise-scale AI solutions in production environments.
• Strong communication and stakeholder management skills.

AI Developer  

Primary Skill-set (Must have) 

• Generative AI Expertise: 2-3 years of experience in designing and implementing generative AI solutions, including knowledge of various generative and autoregressive models. Ability to apply generative AI techniques to diverse use cases such as image generation, text generation, and creative content synthesis. 

• 2 years of experience in prompt engineering, fine tuning, agentic framework, GenAI SDK’s 

• 1-2 years of experience in Agentic AI frameworks like Autogen, Lanngraph, MS Agent SDK, A2A, MCP, A2P, memory concepts, multi agent orchestration 

• 7+ years of experience in Python 

• 5+ years of experience in software development 

• Azure Proficiency: 3-5 years of experience with Azure cloud services relevant to AI, including Azure Machine Learning, Azure Cognitive Services, Azure Databricks, and Azure Kubernetes Service (AKS). 2+ years of experience in Azure's capabilities to architect end-to-end AI solutions and optimize performance. 

• Architecture Design: 3-5 years of skills with the ability to design scalable, reliable, and cost-effective architectures for AI solutions. Proficiency in designing distributed systems, microservices architectures, and containerized solutions using technologies such as Docker and Kubernetes. 

Secondary Skills (Good to have) 

• Security and Compliance: Understanding of security principles and best practices in AI development, with the ability to implement security controls, encryption mechanisms, and access management policies to protect AI models and sensitive data. 

• Integration and Deployment: Proficiency in implementing CI/CD pipelines, automation scripts, and infrastructure as code (IaC) using tools such as Azure DevOps, Terraform, or Ansible. Experience in containerization and orchestration of AI workloads using Docker and Kubernetes. 

• Software Development: Strong programming skills in languages such as Python, with experience in developing AI applications, RESTful APIs, and microservices architectures. Familiarity with software development methodologies such as Agile or Scrum. 

• Communication and Presentation: Excellent communication skills with the ability to convey complex technical concepts to non-technical stakeholders. Experience in preparing and delivering technical presentations, architecture diagrams, and documentation to communicate architectural decisions and design rationale effectively. 

PRINCIPAL AI ENGINEER @ METADOME.AI

Company Description

Metadome.ai builds frontier AI models that transform text, drawings, and CAD into production-ready, interactive 3D experiences. The company advances a full generative pipeline—text-to-CAD, 2D-to-3D

reconstruction, CAD completion and harmonization, and real-time interactive rendering—engineered for the precision required in the physical world. Its technology currently powers the modernization of

OEM aftersales for more than 30 automotive and heavy-equipment manufacturers worldwide, delivering accurate, scalable, and fast 3D solutions. Metadome.ai’s platform enables shoppable 3D parts, step-by-step repair animations, and a headless API that feeds consistent 3D assets into commerce, dealer, training, and service systems. The broader mission is to allow anyone to move from an idea, drawing, or specification to a production-grade 3D model and beyond in seconds.

Role Description

As a Principal AI Engineer — Generative CAD & 3D, you will lead the design, development, and deployment of advanced AI models that convert text, 2D drawings, and CAD files into engineering-grade 3D content. You will architect end-to-end generative pipelines, including

text-to-CAD, 2D-to-3D reconstruction, CAD completion, and real-time rendering, collaborating closely with product, design, and engineering teams to ship robust production systems. Day-to-day, you will experiment with novel neural network architectures, optimize model performance on large-scale CAD datasets, write high-quality production code, and guide the integration of AI services into customer-facing platforms. You will mentor other engineers, establish best practices for AI development, and contribute to technical strategy and roadmap. This is a full-time, hybrid role based in Bengaluru, with a mix of on-site collaboration and work-from-home flexibility.

Qualifications

• Strong foundation in Computer Science and Software Development, including data structures, algorithms, system design, and production-grade coding in languages such as Python, C++, or similar.
• Deep expertise in Neural Networks and Pattern Recognition, with hands-on experience designing, training, and deploying modern deep learning architectures for complex, high-dimensional data.
• Experience with Natural Language Processing (NLP), including working with text encoders, multimodal models, and integrating language understanding into generative workflows.
• Advanced degree (Master’s or PhD) in Computer Science, Electrical Engineering, Applied Mathematics, or a related field, or equivalent practical experience in AI/ML research and engineering.
• Background in 3D geometry, CAD, computer graphics, or related domains, with familiarity in 3D representations, mesh processing, and rendering pipelines.

We are seeking Generative AI Developers with strong Python programming and AI/ML expertise to build, deploy, and optimize LLM-powered applications. The role involves developing RAG solutions, AI agents, and enterprise GenAI applications while collaborating with cross-functional teams.

Key Responsibilities

• Develop and enhance Generative AI applications using LLMs and AI frameworks.
• Build and optimize RAG pipelines, vector search, and AI-powered workflows.
• Design effective prompts and fine-tune models using techniques such as LoRA and QLoRA.
• Develop REST APIs and integrate AI capabilities into enterprise applications.
• Deploy, monitor, and maintain AI solutions in cloud and containerized environments.
• Ensure code quality through testing, debugging, documentation, and code reviews.
• Follow Responsible AI, security, and data governance practices.

Required Technical Skills

• Strong proficiency in Python, OOP, APIs, debugging, and software development best practices.
• Good understanding of Data Structures & Algorithms, complexity analysis, and problem-solving.
• Hands-on experience with LLMs, Prompt Engineering, RAG, AI Agents, and embeddings.
• Experience with LangChain, LangGraph, LlamaIndex, Hugging Face, or similar frameworks.
• Knowledge of vector databases, semantic/hybrid search, and retrieval architectures.
• Experience with PyTorch, TensorFlow, or Keras.
• Familiarity with Docker, Git, CI/CD, and cloud platforms (Azure/AWS/GCP).
• Understanding of AI governance, data privacy, and Responsible AI principles.

Preferred Skills

• Experience with Agentic AI frameworks (CrewAI, AutoGen, Semantic Kernel).
• Exposure to Azure AI Foundry, Databricks, or enterprise AI platforms.
• Knowledge of multimodal AI applications.

Qualifications

• Bachelor's or Master's degree in Computer Science, AI, Data Science, or a related field.
• 5 years of software development experience, including AI/ML or Generative AI projects.
• Experience building and deploying production-grade AI solutions.

Assessment Focus Areas

Candidates will be evaluated on:

• Python coding and problem-solving
• Data Structures & Algorithms
• LLMs, RAG, and Agentic AI concepts
• API development and system design
• Cloud deployment and AI solution architecture

About the Role:

We are looking for an ideal candidate with 5+ years of experience in Data Science / Machine Learning, with strong hands-on experience in Generative AI, Large Language Models (LLMs), NLP, and AI-powered applications. The candidate should be comfortable working across the complete AI lifecycle—from understanding business requirements and experimenting with models to building, evaluating, deploying, and monitoring production-grade GenAI solutions.

The role requires a combination of strong technical expertise, business understanding, problem-solving ability, and stakeholder management skills.

Key Responsibilities:

Generative AI & LLM

·      Design, develop, and deploy Generative AI and LLM-based solutions for enterprise use cases.

·      Work with models such as OpenAI, Azure OpenAI, Llama, Mistral, Gemini, or equivalent LLM platforms.

·      Develop applications using prompt engineering, structured outputs, function/tool calling, and LLM orchestration.

·      Design and implement Retrieval-Augmented Generation (RAG) solutions.

·      Work with vector databases and semantic search for enterprise knowledge retrieval.

·      Develop and evaluate AI agents and multi-step AI workflows.

·      Implement techniques such as prompt optimization, context management, grounding, and hallucination reduction.

·      Develop AI solutions for text classification, summarization, information extraction, question answering, document intelligence, and other enterprise use cases.

Machine Learning & Data Science

·      Develop and optimize traditional Machine Learning and statistical models where appropriate.

·      Perform data exploration, feature engineering, model selection, training, validation, and evaluation.

·      Apply appropriate ML and statistical techniques to solve business problems.

·      Work with structured, unstructured, and semi-structured data.

·      Develop scalable data pipelines to support AI/ML solutions.

·      Collaborate with Data Engineers to prepare and manage data for AI applications.

AI Evaluation & Productionization

·      Design evaluation frameworks to measure LLM accuracy, relevance, groundedness, toxicity, latency, and cost.

·      Implement guardrails and responsible AI practices.

·      Monitor model and application performance in production.

·      Identify model/data drift and implement appropriate improvement strategies.

·      Optimize AI solutions for performance, scalability, reliability, and cost.

·      Support deployment and productionization of AI/ML solutions.

·      Client & Delivery Responsibilities

·      Work closely with the CEO, Delivery team, Solution Architects, Engineering teams, and clients to understand business problems and identify AI opportunities.

·      Translate business requirements into practical AI/ML solutions.

·      Participate in client discussions, solution presentations, technical workshops, and POCs.

·      Develop rapid prototypes and demonstrate the feasibility of GenAI solutions.

·      Convert successful POCs into scalable, production-ready applications.

·      Provide technical guidance and contribute to AI solution architecture.

·      Prepare technical documentation, solution approaches, and project estimates where required.

·      Stay current with developments in Generative AI, LLMs, Agentic AI, and AI engineering.

Required Skills:

·       5+ years of hands-on experience in Data Science, Machine Learning, AI, or a related field.

·      Strong practical experience in Generative AI and LLM-based applications.

·      Strong proficiency in Python.

·      Strong understanding of Machine Learning and statistical concepts.

·      Hands-on experience with:

o       LLMs

o       Prompt Engineering

o       RAG

o       Vector Databases

o       Embeddings

o       Semantic Search

o       LLM Evaluation

o       AI Guardrails

·      Experience with frameworks/tools such as LangChain, LangGraph, LlamaIndex, or equivalent.

·      Experience with APIs and integrating LLMs into enterprise applications.

·      Strong SQL and data handling skills.

·      Experience working with large and complex datasets.

·      Strong understanding of NLP concepts.XX

Technical Skills:

·      Experience with OpenAI / Azure OpenAI / AWS Bedrock / Google Vertex AI.

·      Experience with vector databases such as Pinecone, Weaviate, Milvus, FAISS, or equivalent.

·      Experience with Databricks, Snowflake, or cloud data platforms.

·      Experience with Docker and CI/CD.

·      Exposure to AWS, Azure, or GCP.

·      Experience with ML/AI deployment and MLOps.

·       Knowledge of AI security, data privacy, governance, and responsible AI.

·      Experience building AI Agents / Agentic AI workflows.

·      Experience with multimodal AI is an added advantage

Key Competencies

·      Strong analytical and problem-solving ability.

·      Ability to translate business problems into practical AI solutions.

·      Strong communication and presentation skills.

·      Ability to interact confidently with senior stakeholders and clients.

·      Strong ownership and delivery mindset.

·      Ability to work independently in a fast-paced environment.

• Strong experimentation and innovation mindset.
• Ability to balance technical feasibility, business value, scalability, and cost.

Required Education & Experience:

·      Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Statistics, Mathematics, Engineering, or a related discipline

🚀 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

Job Description – Python & Generative AI Engineer

Python & Generative AI Engineer

Location: Bangalore, India

Experience: 4+ Years

Employment Type: Full-time

Job Summary

We are looking for an experienced Python & Generative AI Engineer with 4+ years of software development experience and strong hands-on expertise in Python, LLMs, Generative AI, and AI application development.

The ideal candidate should be comfortable building production-grade AI solutions, integrating LLMs with enterprise applications, and developing scalable APIs and services using Python.

Key Responsibilities

• Design, develop, and deploy Generative AI applications using Python and modern AI/ML frameworks.
• Work with Large Language Models (LLMs) such as GPT, Claude, Gemini, Llama, or similar models.
• Develop RAG (Retrieval-Augmented Generation) pipelines using vector databases and embedding models.
• Build AI-powered applications using frameworks such as LangChain, LangGraph, LlamaIndex, or equivalent.
• Develop scalable REST APIs and backend services using FastAPI, Flask, or Django.
• Integrate LLM APIs, prompt engineering, function/tool calling, and structured outputs into enterprise applications.
• Work with vector databases such as FAISS, Pinecone, Weaviate, Milvus, or Azure AI Search.
• Implement document processing, chunking, embeddings, semantic search, and knowledge retrieval solutions.
• Evaluate LLM responses for accuracy, relevance, hallucination, latency, and cost.
• Build and maintain production-ready AI pipelines with appropriate monitoring, logging, security, and error handling.
• Collaborate with data scientists, ML engineers, software engineers, and business stakeholders to deliver AI solutions.
• Write clean, reusable, testable, and well-documented Python code.
• Participate in architecture discussions, code reviews, testing, deployment, and production support.

Required Skills

• 4+ years of experience in software/Python development.
• Strong proficiency in Python and object-oriented programming.
• Hands-on experience with Generative AI and LLM-based applications.
• Experience with OpenAI/Azure OpenAI, Anthropic, Google Gemini, or open-source LLMs.
• Strong understanding of Prompt Engineering and LLM application patterns.
• Experience implementing RAG pipelines.
• Knowledge of embeddings, vector databases, semantic search, and document retrieval.
• Experience with LangChain, LangGraph, LlamaIndex, or similar frameworks.
• Strong experience developing REST APIs using FastAPI/Flask/Django.
• Familiarity with Git, CI/CD, Docker, and cloud platforms such as AWS, Azure, or GCP.
• Good understanding of SQL and databases.
• Strong problem-solving and communication skills.

Good to Have

• Experience with AI agents / Agentic AI and tool/function calling.
• Experience with multi-agent frameworks.
• Knowledge of MLOps/LLMOps and model evaluation.
• Experience with Kubernetes and containerized deployments.
• Knowledge of NLP, machine learning, or deep learning.
• Experience with Azure AI, AWS Bedrock, Amazon SageMaker, or Google Vertex AI.
• Understanding of responsible AI, data privacy, and LLM security.

Education

Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related field.

Strong AI/ML Engineer Profile

Mandatory (Experience) : Must have 3+ years of experience in software engineering with atleast 1+ years in GenAI application development and production deployment

Mandatory (GenAI Application Development): Must have proven experience building GenAI applications covering RAG pipelines, multi-agent systems, Text2SQL, and fine-tuning

Mandatory (Production GenAI Deployment): Must have expertise deploying production-grade GenAI applications including model evaluation, optimisation, and ownership of full production rollouts

Mandatory (ML & Data Science Tooling): Must have strong hands-on experience with core ML and data science tools including pandas, scikit-learn, and PyTorch

Mandatory (Cloud ML Infrastructure): Must have experience building and deploying production-grade ML workloads on at least one of AWS, Azure, or GCP

Mandatory (Communication): Must have strong English communication skills with the ability to work across time zones and collaborate cross-functionally with product, engineering, and business stakeholders

Mandatory (Note 1) : Role is Hybrid, WFH flexibility as well upto 6 days a month

Mandatory (Note 2) : CTC is inclusive of 10% variable

Mandatory (Note 3): Candidates should be available to join within May 31st or June first week max

Job Description – AI Engineer (End-to-End Development & Deployment)

Role Summary

We are looking for an AI Engineer with hands-on experience in designing, developing, deploying, and maintaining Generative/Agentic AI solutions in production. The ideal candidate should have end-to-end ownership of AI applications, from development to deployment, monitoring, and optimization.

Key Responsibilities

●        Design, build, and deploy Generative/Agentic AI solutions.

●        Develop applications using LLMs, RAG, AI agents, and vector databases.

●        Build scalable APIs and integrate AI solutions with enterprise applications.

●        Implement CI/CD pipelines, containerization, and MLOps best practices.

●        Monitor, optimize, and maintain production AI systems.

●        Collaborate with cross-functional teams to deliver business-driven AI solutions.

Required Skills

●       Strong programming skills in Python.

●       Experience with vector databases (e.g., Pinecone, FAISS, ChromaDB) and graph memory systems

●       Knowledge of atleast one agent development framework: Google ADK (preferred), LangChain/LangGraph/LlamaIndex, CrewAI

●       Experience with LLMs, RAG, GenAI, AgenticAI Agents

●       Hands-on experience with FastAPI, and REST APIs.

●       Knowledge of Docker, Kubernetes, Git, CI/CD.

●       Experience with AWS, Azure, or GCP. 

●       Experience with security compliance, monitoring and observability tools such as AWS CloudWatch, Azure Monitor, Google Cloud Monitoring.