AI Implementation Engineer

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

Job Summary/ Job Opportunity:

This is an excellent opportunity for an ideal candidate with a high level of technical proficiency and meeting the below mentioned criteria -- • Strong experience in Machine Learning, Deep Learning, Generative AI, and Large Language Models (LLMs). • Hands-on experience building and deploying production-grade solutions using Azure OpenAI, OpenAI, LangChain, LangGraph, Semantic Kernel, LlamaIndex, and Agentic AI frameworks. • Strong expertise in Python, API development, microservices, and cloud-native architectures. • Experience designing and implementing RAG solutions, vector databases, embeddings, knowledge retrieval systems, and AI copilots. • Experience with Azure cloud services, MLOps, CI/CD pipelines, monitoring, and model lifecycle management. • Strong understanding of AI governance, responsible AI, security, compliance, and model evaluation frameworks. • Ability to lead technical discussions, provide architectural recommendations, mentor team members, and interact with business stakeholde

Key Objectives and Major Responsibilities:

• Design, develop, and implement scalable AI/ML and Generative AI solutions for enterprise applications. • Lead development of intelligent applications leveraging LLMs, RAG pipelines, AI agents, and document intelligence solutions. • Collaborate with business stakeholders, architects, and product teams to translate business requirements into technical solutions. • Design and optimize data pipelines, vector search solutions, embeddings, and retrieval mechanisms. • Build and maintain REST APIs, microservices, and cloud-native AI applications. • Ensure best practices in coding standards, performance optimization, security, scalability, and maintainability. • Drive AI solution deployment using MLOps practices, CI/CD pipelines, monitoring, and observability frameworks. • Perform code reviews, mentor junior developers, and contribute to capability building within the team

Key Capabilities and Competencies:

Knowledge, Skills, Qualification and Experience

• Degree in B.Tech/M.Tech (Computer Science/IT/Data Science) or related discipline preferred, with 3–4 years of relevant experience in AI/ML, GenAI and total 5-7 years of experience. • Proficiency in Python and hands-on experience with ML libraries (scikit-learn, TensorFlow, PyTorch) and GenAI frameworks/tools. • Strong understanding of machine learning, deep learning, LLMs, prompt engineering, and techniques like RAG and fine-tuning. • Experience with data processing, embeddings, vector databases, APIs, and building scalable AI driven applications. • Good communication skills, ability to work on multiple projects, and eagerness to learn and adapt to evolving AI technologies. 

Role Overview 

We are looking for an AI Engineer to design, build, and ship production AI systems, including agentic AI applications, for enterprise clients. This is a hands-on engineering role: you will write production code, build and evaluate models and agents, and work closely with architects and product teams to take solutions from prototype to scale. 

Key Responsibilities 

Design and build agentic AI systems: agent workflows, tool/function-calling, memory, and human-in-the-loop patterns. Build and productionise RAG pipelines, prompt-based applications, and LLM integrations across providers. Develop and maintain data and ML pipelines: feature engineering, model training, evaluation, and monitoring. Integrate AI systems with enterprise applications (CRMs, ERPs, ITSM tools) via APIs, events, and MCP-based tool servers. Implement guardrails, prompt-injection defences, and evaluation frameworks to keep AI systems safe and reliable in production. 

Write clean, tested, production-grade code and participate actively in code and design reviews. 

Collaborate with architects, product managers, and delivery teams to translate requirements into working AI solutions. Troubleshoot and optimise AI systems for accuracy, latency, and cost in production. 

Required Qualifications 

8–12 years of hands-on software engineering experience, with a strong, unbroken technical track record. Hands-on experience building and shipping AI/ML systems in production, not just POCs. 

Practical experience with agentic AI systems and at least one major agent framework (LangGraph, CrewAI, AutoGen, OpenAI Agents SDK, Bedrock Agents/Strands, or Semantic Kernel). 

Experience with LLM/GenAI systems: RAG pipelines, prompt engineering, structured outputs, and tool calling across providers. 

Strong Python skills (TypeScript/Node.js a plus), with production-grade testing, CI/CD, and API design practices. Working knowledge of ML fundamentals: model evaluation, feature engineering, and experimentation. Cloud-native experience on AWS and/or Azure: containers, serverless, event backbones, and vector databases. Understanding of LLM safety and reliability practices: guardrails, prompt-injection defences, and observability. 

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. 

Hiring for Junior AI Engineer

Exp : 4 - 6 yrs

Edu : BE/B.Tech/MCA

Work Location : Pune WFO

Skills :

- Min 3 years strong programming experience in Python is a MUST

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

- Experience with cloud platforms (AWS/Azure/GCP)

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.

About the role

We are building AI systems that read, understand and act on real business documents, bank statements, financial reports, policy documents and forms and putting them into production where accuracy and cost both matters.

This is not a research role and it is not a prompt-writing role. You will own features end to end: pick and deploy open-source models, build the pipelines around them, measure whether they actually work on our documents, drive the cost per document down, and keep the whole thing running in production.

You will work closely with the engineering and product teams, and your work will be directly used by business users from day one.

What you will do

Deploy and evaluate open-source models

• Select, deploy and benchmark open-source LLMs and vision-language models for specific, narrow use cases not general chat.
• Build evaluation sets from real documents and define what "good" means numerically (field-level accuracy, extraction recall, hallucination rate) before shipping.
• Run structured comparisons between models and approaches, and write up the trade-offs so the team can make a decision.
• Apply quantization, batching and other optimizations to fit models into a sensible GPU budget.

Build and optimize AI orchestration

• Design multi-step pipelines that combine deterministic code, ML models and LLM calls and know when not to use an LLM.
• Optimize for latency, cost and reliability: caching, batching, request routing, fallback tiers, retries and graceful degradation.
• Instrument pipelines so failures are visible and traceable rather than silent.

Ship to production

• Package models and services with Docker, expose them behind clean APIs, and deploy them to our GPU and CPU infrastructure.
• Handle the unglamorous production concerns: cold starts, timeouts, concurrency limits, versioning, rollback and monitoring.
• Own on-call-style responsibility for the AI features you build, including cost tracking.

Must-have skills

Programming & engineering

• Strong Python: type hints, async/await, dataclasses/Pydantic, clean module design, testing.
• REST API development with FastAPI (or Flask/Django with a willingness to move to FastAPI).
• Git, code review discipline, and the ability to write code someone else can maintain.
• Comfortable in Linux and on the command line.

Machine learning fundamentals

• Working knowledge of PyTorch and the Hugging Face ecosystem (transformers, tokenizers, accelerate).
• Understanding of inference-time concepts: tokenization, context windows, batching, precision (FP16/BF16/INT8), memory footprint.
• Ability to read a model card and a paper well enough to judge whether a model fits a use case.

Document processing

• Hands-on experience with at least two of: pypdfium2, PyMuPDF, pdfplumber, pdfminer.six, Docling, Unstructured, Surya, DocTR, LayoutLM family.
• Practical OCR experience (Tesseract, PaddleOCR, or a cloud OCR) and an understanding of when OCR is the wrong tool.
• Experience extracting tables from PDFs and dealing with merged cells, multi-line rows, and inconsistent column layouts.

Strongly preferred

You will be a much stronger candidate with any of these. We do not expect all of them.

Model serving & optimization

• vLLM, TGI, Ollama, llama.cpp, or Triton Inference Server.
• Quantization formats and tooling: GGUF, AWQ, GPTQ, bitsandbytes, ONNX Runtime, INT8 export.
• Serverless GPU platforms: Modal, RunPod, Replicate, Baseten including cold-start and container-lifecycle management.
• LoRA / QLoRA fine-tuning with PEFT for narrow, task-specific improvements.

Vision-language models

• Practical use of open VLMs: Qwen2.5-VL, InternVL, Granite Vision, Molmo, Phi-Vision, or similar.
• Awareness of where VLMs hallucinate especially on numeric and financial content and patterns for constraining them (using the model for layout only, sourcing values from the text layer, constrained decoding).

Orchestration & pipelines

• Workflow orchestration: Dagster, Airflow, Prefect, or Temporal.
• Async job patterns: Celery, RQ, or platform-native spawn/poll patterns.
• LLM orchestration frameworks (LangGraph, LlamaIndex, Haystack) with the judgement to know when plain Python is a better answer.
• Structured output enforcement: Instructor, Outlines, XGrammar, JSON schema / tool-use modes.

Evaluation & observability

• Building golden datasets and regression suites for extraction tasks.
• Eval tooling: promptfoo, DeepEval, Ragas, or in-house harnesses.
• LLM tracing and monitoring: Langfuse, Arize Phoenix, LangSmith, OpenTelemetry.

Nice extras

• Rule engines and policy evaluation (Open Policy Agent / Rego, Drools, rule-engine).
• Experience in fintech, lending, insurance or accounting documents.
• Handling of PII and data-security practices in document pipelines.
• Contributions to open-source ML or document-processing projects.

Why join us

• Real production ownership from month one your work goes to actual users, not a demo.
• Genuinely hard technical problems in document AI, not wrappers over an API.
• Small team, short decision cycles, direct access to leadership.
• Budget and freedom to evaluate and adopt new open-source models as they land.

To apply: send your CV along with a short note on one AI system you have taken to production what it did, what the accuracy was, and what broke.

Hiring for AI Engineer

Exp: 5 - 10 yrs

Edu : BE/B.Tech/MCA

Work Location : Pune / Mumbai

Skill Set:

Total experience ranging from 5–10 years in software engineering/AI roles

Min 5 years strong programming experience in Python is a MUST

Min 3.5 years hands-on experience in AI with LLMs, RAG pipelines, and AI frameworks

2+ years shipping LLM systems in production

Experience with cloud platforms (AWS/Azure/GCP)

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.

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