Junior Gen AI

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)

Strong AI Engineer / Machine Learning Engineer profiles.

2

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.

3

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

4

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

5

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.

6

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

7

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.

8

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

9

Mandatory (CTC) – The CTC breakup offered will be 75% fixed + 25% variable, as per company policy.

10

Mandatory (Age) - Candidate's Age should be below 28 Years

11

Preferred (Experience 1) – Experience with MLFlow, Kubeflow, Airflow, Prefect, Feature Stores, Model Registry, or MLOps/LLMOps frameworks.

12

Preferred (Experience 2) – Experience working with Vector Databases, Spark, PySpark, distributed ML pipelines, large-scale data processing, or real-time ML systems..

13

Preferred (Experience 3) – Familiarity with Docker, Kubernetes, Azure, AWS, GCP, cloud-native AI deployments, and scalable ML architecture.

14

Preferred (Company) – Candidates from AI-first startups, Fintech, Banking, Lending, Fraud Analytics, Risk Analytics, Product Companies, SaaS organizations, or data-driven technology companies

15

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

Strong AI Engineer / Machine Learning Engineer profiles.

2

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.

3

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

4

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

5

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.

6

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

7

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.

8

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

9

Mandatory (CTC) – The CTC breakup offered will be 75% fixed + 25% variable, as per company policy.

10

Mandatory (Age) - Candidate's Age should be below 28 Years

Strong AI Engineer / Machine Learning Engineer profiles.

2

Mandatory (Experience 1) – Must have minimum 5+ years of hands-on experience in Data Science, Machine Learning, Applied AI, NLP, Deep Learning, or Generative AI solutions.

3

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

4

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

5

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.

6

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

7

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.

8

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

9

Mandatory (CTC) – The CTC breakup offered will be 75% fixed + 25% variable, as per company policy.

10

Mandatory (Age) - Candidate's Age should be below 30 Years

11

Preferred (Experience 1) – Experience with MLFlow, Kubeflow, Airflow, Prefect, Feature Stores, Model Registry, or MLOps/LLMOps frameworks.

12

Preferred (Experience 2) – Experience working with Vector Databases, Spark, PySpark, distributed ML pipelines, large-scale data processing, or real-time ML systems..

13

Preferred (Experience 3) – Familiarity with Docker, Kubernetes, Azure, AWS, GCP, cloud-native AI deployments, and scalable ML architecture.

14

Preferred (Company) – Candidates from AI-first startups, Fintech, Banking, Lending, Fraud Analytics, Risk Analytics, Product Companies, SaaS organizations, or data-driven technology companies

15

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

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

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 Data Scientist / AI Engineer / Generative AI Engineer profile.

2

Mandatory (Experience 1) - Must have 3+ years of hands-on experience in Data Science, Artificial Intelligence, Machine Learning, Deep Learning, NLP, or Generative AI application development.

3

Mandatory (Experience 2) - Must have strong hands-on experience in Python programming, backend development, API development, and production-grade application support.

4

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

5

Mandatory (Experience 4) - Must have hands-on experience in NLP use cases such as text classification, sentiment analysis, entity recognition (NER), semantic search, embeddings, or document understanding.

6

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

7

Mandatory (Experience 6) - Must have hands-on experience building or implementing Retrieval Augmented Generation (RAG) solutions, vector search, semantic search, or knowledge-based AI applications.

8

Mandatory (Experience 7) - Must have experience with Prompt Engineering and Generative AI frameworks such as LangChain, LangGraph, AI Agents, Azure OpenAI, or similar technologies.

9

Mandatory (Experience 8) - Must have experience developing, consuming, or integrating APIs using Python frameworks such as FastAPI, Flask, or similar technologies.

10

Mandatory (CTC) - The CTC breakup offered will be 75% fixed + 25% variable, as per company policy.

11

Preferred (Experience 1) - Experience with LLMOps/MLOps tools for monitoring, evaluation, experimentation, and versioning of AI models.

12

Preferred (Experience 2) - Exposure to Azure OpenAI, Azure Kubernetes Service (AKS), Kubernetes, cloud-native AI deployments, or distributed systems.

Strong AI Engineer / Machine Learning Engineer profiles.

2

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.

3

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

4

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

5

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.

6

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

7

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.

8

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

9

Mandatory (CTC) – The CTC breakup offered will be 75% fixed + 25% variable, as per company policy.

10

Mandatory (Age) - Candidate's Age should be below 30 Years

11

Preferred (Experience 1) – Experience with MLFlow, Kubeflow, Airflow, Prefect, Feature Stores, Model Registry, or MLOps/LLMOps frameworks.

12

Preferred (Experience 2) – Experience working with Vector Databases, Spark, PySpark, distributed ML pipelines, large-scale data processing, or real-time ML systems..

13

Preferred (Experience 3) – Familiarity with Docker, Kubernetes, Azure, AWS, GCP, cloud-native AI deployments, and scalable ML architecture.

14

Preferred (Company) – Candidates from AI-first startups, Fintech, Banking, Lending, Fraud Analytics, Risk Analytics, Product Companies, SaaS organizations, or data-driven technology companies

15

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

Roles & Responsibilities

• Design and develop intelligent AI-based applications using advanced NLP and LLM techniques to solve real-world business challenges in financial services.
• Build and optimize Retrieval-Augmented Generation (RAG) pipelines leveraging structured and unstructured financial data.
• Integrate and orchestrate LLMs/SLMs for question-answering, summarization, semantic search, and document understanding.
• Develop and maintain RESTful APIs (sync and async) to serve NLP models and chatbot interfaces using frameworks like FastAPI, Flask, etc.
• Should have knowledge of advanced prompting techniques.
• Implement semantic search, hybrid search, and text retrieval systems using Elasticsearch and vector databases (e.g., FAISS, Pinecone, Weaviate).
• Perform NLP tasks such as entity recognition, text classification, intent detection, embedding generation, and sentiment analysis where required.
• Monitor and fine-tune LLM/SLM performance with real-world user data to improve relevance, latency, and accuracy.
• Exposure to LLMOps tools for monitoring, evaluation, and versioning of AI models in production.
• Build, train, and evaluate deep learning models for NLP tasks including classification, NER, summarization, and embedding generation.
• Develop traditional machine learning models (e.g., regression, decision trees, clustering) for structured data analysis and prediction tasks.
• Interact with cross-functional teams to understand system issues and follow up with respective teams to get them fixed.
• Understand and identify areas of improvement across businesses and participate in solution identification and implementation.
• Should be able to work as an Individual Contributor on new and existing projects.
• Positive and problem-solving attitude, must work as an independent contributor.

Ideal Candidate

1.Strong Data Scientist / AI Engineer / Generative AI Engineer profile.

2.Mandatory (Experience 1) - Must have 3+ years of hands-on experience in Data Science, Artificial Intelligence, Machine Learning, Deep Learning, NLP, or Generative AI application development.

3.Mandatory (Experience 2) - Must have strong hands-on experience in Python programming, backend development, API development, and production-grade application support

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

5.Mandatory (Experience 4) - Must have hands-on experience in NLP use cases such as text classification, sentiment analysis, entity recognition (NER), semantic search, embeddings, or document understanding.

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

.7.Mandatory (Experience 6) - Must have hands-on experience building or implementing Retrieval Augmented Generation (RAG) solutions, vector search, semantic search, or knowledge-based AI applications.

8.Mandatory (Experience 7) - Must have experience with Prompt Engineering and Generative AI frameworks such as LangChain, LangGraph, AI Agents, Azure OpenAI, or similar technologies.

9.Mandatory (Experience 8) - Must have experience developing, consuming, or integrating APIs using Python frameworks such as FastAPI, Flask, or similar technologies.

10.Mandatory (CTC) - The CTC breakup offered will be 75% fixed + 25% variable, as per company policy.

11.Preferred (Experience 1) - Experience with LLMOps/MLOps tools for monitoring, evaluation, experimentation, and versioning of AI models.

12.Preferred (Experience 2) - Exposure to Azure OpenAI, Azure Kubernetes Service (AKS), Kubernetes, cloud-native AI deployments, or distributed systems.

13.Preferred (Experience 3) - Experience working with PostgreSQL, MongoDB, Redis, Kafka, or large-scale data platforms.

14.Preferred (Experience 4) - Familiarity with Docker, Kubernetes, cloud platforms, and scalable deployment architecture.

15.Preferred (Company) - Candidates from AI-first startups, product companies, SaaS organizations, fintech, or data-driven technology companies.

16.Mandatory ( Age ) - Candidate Should be Below 28 Years.

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

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