Generative AI Engineer at TrueBlue · Bengaluru (Bangalore), Delhi, Gurugram, Noida, Ghaziabad, Faridabad · 4 - 13 years · ₹11L - ₹35L / yr · Posted 14 Aug 2026
Python
Docker
GitHubGenerative AI Engineer
Role Overview:
You will be responsible for the hands-on development, coding, and deployment of AI-powered features. Your focus is on writing clean, efficient code to integrate LLMs into our existing tech stack, building robust data pipelines for RAG, and ensuring the reliability of model outputs through rigorous testing and optimization.
Key Responsibilities
- Application Implementation: Code and integrate LLM APIs (OpenAI, Anthropic, etc.) or local models into backend services using Python, FastAPI, etc.,
- MCP Server Development: Design and implement custom MCP servers using the official SDKs (Python/TypeScript) to expose internal databases, APIs, and file systems to AI agents.
- RAG Implementation: Build and maintain the "plumbing" for Retrieval-Augmented Generation—specifically coding the data ingestion scripts, text chunking logic, and metadata filtering.
- Vector DB Management: Perform day-to-day operations on vector databases (Pinecone, Milvus, etc.), including indexing, querying, and optimizing search retrieval.
- Prompt Programming: Develop, version-control, and refine complex prompt templates (using Jinja2 or similar) to ensure consistent structured outputs (JSON/YAML).
- Agent Development: Implement multi-step workflows using LangChain, LangGraph, CrewAI etc.,, focusing on tool-calling logic and error handling.
- Evaluation & Testing: Build automated test suites to detect "hallucinations" and measure accuracy using frameworks.
- Performance Tuning: Implement caching layers and streaming responses to reduce latency and improve the end-user experience; Token optimization.
- Data Pre-processing: Clean and tokenize datasets for model fine-tuning or high-quality context retrieval.
Technical Skills (The "Execution" Stack)
- Language: Advanced Python (Asyncio, Pydantic) and optional TypeScript/Node.js (for full-stack integration).
- AI Frameworks: Hands-on experience with any of LangChain, LlamaIndex, and Hugging Face Transformers. RAG and Vector search concepts.
- Data Handling: Proficiency in SQL and handling unstructured data formats (PDFs, Markdown, JSON).
- Deployment: Practical experience with Docker, GitHub Actions (CI/CD), and experience with OpenTelemetry, LangSmith, Weights & Biases etc., Understanding of evaluation/guardrails.
- MCP/API Proficiency: Deep understanding of RESTful APIs, Streaming HTTP, MCP server vs client, JSONRPC
Shubham Vishwakarma
Full Stack Developer - Averlon
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Amazon Web Services (AWS)Senior Generative AI Engineer
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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.
PythonPOSITION OVERVIEW
We are seeking an experienced Senior Data Scientist & Generative AI Specialist on a contractual basis to support a premier Germany-based chemical manufacturing enterprise. In this role, you will lead the end-to-end design, development, and deployment of production-grade GenAI applications, multi-modal LLM workflows, and advanced retrieval platforms tailored to complex industrial and enterprise data ecosystems.
Working closely with cross-functional global teams, you will build robust backend microservices, implement state- of-the-art RAG/GraphRAG architectures, and leverage cloud-native AI infrastructure (Azure, Vector DBs, Knowledge Graphs) to drive operational efficiency and data-driven innovation.
KEY RESPONSIBILITIES
- GenAI & LLM System Engineering: Design, build, and deploy production-grade multi-modal GenAI applications processing text, structured technical documentation, images, and telemetry data.
- Advanced RAG & Graph Architecture: Implement cutting-edge Retrieval-Augmented Generation (RAG) and GraphRAG pipelines using document parsing frameworks, custom embeddings, vector databases, and knowledge graphs to capture complex domain relationships.
- Scalable Backend Development: Architect high-throughput, low-latency microservice APIs using Python, FastAPI, and Flask, leveraging asynchronous programming (asyncio) and strict type validation (Pydantic) for long-running LLM processes.
- Agentic Systems & Azure Ecosystem: Build autonomous agent systems using modern frameworks (MCP, A2A) and orchestrate enterprise workflows across the Microsoft Azure AI ecosystem (Azure AI Foundry, AI Search, Document Intelligence, Databricks).
- Model Optimization & Evaluation: Execute systematic LLM fine-tuning, prompt optimization, and rigorous evaluation frameworks to assess AI output accuracy, reliability, and business impact against industrial requirements.
- Data Layer Management: Architect and maintain enterprise database layers combining SQL (PostgreSQL) for structured transactional data with specialized vector search engines and graph stores.
- Rapid Prototyping: Utilize AI-assisted development tools (Copilot, Claude Code) to accelerate delivery timelines and rapidly build functional UI prototypes for client feedback.
TECHNICAL QUALIFICATIONS
Core Development & Backend:
• Python Mastery: Deep expertise in writing clean, production-ready Python using asynchronous programming (asyncio), strict type-hinting (Pydantic), and automated testing patterns.
• Backend Microservices: Hands-on experience building microservices with FastAPI and Flask structured to handle asynchronous, long-running AI background tasks.
• Database Engineering: Strong command of PostgreSQL, relational schema design, vector indexing, and knowledge graph paradigms.
Machine Learning & AI Infrastructure:
• Model Expertise: Hands-on experience with leading multi-modal LLM architectures (OpenAI, Anthropic, Google) and domain-specific AI workflows.
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• Agentic Frameworks: Practical experience with open-source agent protocols (MCP, A2A), parameter-efficient fine-tuning (PEFT/LoRA), and model evaluation methodology.
CONTRACT & REMOTE REQUIREMENTS
• Contract Engagement: Contractual structure tailored to project milestones and deliverables.
• 100% Remote Setup: Fully equipped home office with high-speed, secure internet infrastructure.
• Timezone Overlap: Guaranteed 4-hour daily overlap with Central European Time (CET/CEST - Germany) to ensure smooth collaboration with enterprise stakeholders.
• Communication: Fluent professional English communication skills (written and spoken) for asynchronous and real-time technical coordination.
The recruiter has not been active on this job recently. You may apply but please expect a delayed response.
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.
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Mandatory (Experience 1) – Must have minimum 3+ years of hands-on experience in Data Science, Machine Learning, Applied AI, NLP, Deep Learning, or Generative AI solutions.
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Mandatory (Experience 2) – Must have strong hands-on experience in Python programming, SQL, data analysis, feature engineering, model development, and production-grade ML applications.
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Mandatory (Experience 3) – Must have experience working with Machine Learning and Deep Learning frameworks such as PyTorch, TensorFlow, Keras, Scikit-learn, or equivalent.
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Mandatory (Experience 4) – Must have hands-on experience working on NLP, embeddings, semantic search, text classification, document understanding, recommendation systems, or similar AI/ML use cases.
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Mandatory (Experience 5) – Must have experience working with Large Language Models (LLMs) such as GPT, Llama, Mistral, Claude, Gemini, Phi, or similar foundation models.
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Mandatory (Experience 6) – Must have hands-on experience building or implementing RAG (Retrieval Augmented Generation) systems, vector search, knowledge retrieval, embeddings, chunking, indexing, or semantic retrieval solutions.
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Mandatory (Experience 7) – Must have experience working with Git, CI/CD practices, production environments, and scalable AI/ML systems.
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
Machine Learning (ML)PythonStrong 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.
Machine Learning (ML)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.
PythonWe 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
Data ScienceStrong 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.
PythonStrong 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.
Data ScienceMachine Learning (ML)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.
Follow CutshortShubham Vishwakarma
Full Stack Developer - Averlon