Data Scientist / AI Engineer (Python, ML, RAG) at TalentXO · Pune · 3 - 5 years · ₹15L - ₹20L / yr · Profitable · Posted 18 Sep 2026
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.
Shubham Vishwakarma
Full Stack Developer - Averlon
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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.
• Retrieval & Parsing: Proven track record with document extraction frameworks, embedding models, vector search engines, and GraphRAG architectures.
• Cloud Infrastructure: Strong proficiency with Azure AI infrastructure (Foundry, Databricks, AI Search, Document Intelligence).
• 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.
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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.
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Mandatory (Age) - Candidate's Age should be below 28 Years
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Preferred (Experience 2) – Experience working with Vector Databases, Spark, PySpark, distributed ML pipelines, large-scale data processing, or real-time ML systems..
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Preferred (Company) – Candidates from AI-first startups, Fintech, Banking, Lending, Fraud Analytics, Risk Analytics, Product Companies, SaaS organizations, or data-driven technology companies
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Mandatory (CTC) – The CTC breakup offered will be 75% fixed + 25% variable, as per company policy.
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Mandatory (Age) - Candidate's Age should be below 28 Years
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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.
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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 (CTC) – The CTC breakup offered will be 75% fixed + 25% variable, as per company policy.
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Mandatory ( Pedigree) - B.TECH / M.TECH from Tier 1 Colleges (IIT's, NIT's, BITS) are Considered.
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
The recruiter has not been active on this job recently. You may apply but please expect a delayed response.
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.
The recruiter has not been active on this job recently. You may apply but please expect a delayed response.
PythonGenerative 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
Follow CutshortShubham Vishwakarma
Full Stack Developer - Averlon