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Netscribes

AI ML Engineer

Netscribes
Bengaluru, India
3-5 Years
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Job Description

We are looking for a skilled ML/AI Specialist with a strong blend of classical Machine Learning and Agentic AI expertise. The ideal candidate will have 3-4 years of hands-on experience building end-to-end ML pipelines and intelligent AI systems spanning classical ML model development, LLM integration, Agentic AI frameworks, Foundation Models, and RAG pipelines. This role sits within our Operations | Data Engineering line of business and offers the opportunity to shape intelligent, production-ready AI solutions at scale.

The Candidate Will Have Responsibilities Across The Following Functions

ML Model Development and Lifecycle:

  • Design, develop, and deploy end-to-end ML pipelines covering data preparation, feature engineering, model training, evaluation, and production deployment.
  • Manage the full ML lifecycle using MLflow or equivalent experiment tracking, model registry, versioning, and performance monitoring.
  • Build, fine-tune, and serve classical ML and deep learning models for classification, regression, NLP, and other use cases.
  • Implement model evaluation frameworks to ensure accuracy, fairness, robustness, and continuous improvement post-deployment.

Agentic AI And LLM Engineering

  • Develop Agentic AI workflows using frameworks such as LangChain, LangGraph, AutoGen, or CrewAI, including tool use, multi-agent orchestration, and memory management.
  • Integrate and fine-tune Foundation Models (OpenAI, Anthropic, Mistral, Llama, etc. ) for domain-specific applications.
  • Design and optimise prompts for LLM-powered features, including summarisation, classification, extraction, and reasoning tasks.
  • Build and maintain model serving endpoints for scalable, low-latency inference in production environments.

RAG Pipeline Development

  • Architect and implement end-to-end RAG pipelines for document ingestion, chunking, embedding generation, vector store management, retrieval optimisation, and response synthesis.
  • Integrate vector databases (Pinecone, Weaviate, Qdrant, ChromaDB, or equivalent) for semantic search and knowledge retrieval.
  • Continuously evaluate and improve RAG pipeline quality through relevance scoring, hallucination detection, and ground truth benchmarking.

Collaboration And Delivery

  • Work closely with senior engineers, data scientists, and business stakeholders to understand requirements and translate them into AI solutions.
  • Write clean, modular, reusable, and well-documented code in Python and SQL.
  • Provide regular updates on project status and proactively flag risks or blockers.
  • Participate in code reviews and contribute to best practices for ML/AI engineering.

Requirements

  • Strong proficiency in Python for ML/AI workflows, scripting, and production code development.
  • SQL from fundamental queries to complex transformations, aggregations, and data manipulation.
  • Classical ML feature engineering, model selection, hyperparameter tuning, and model evaluation using Scikit-learn, XGBoost, and MLflow.
  • ML Model Development and Lifecycle Management end-to-end model development covering data preparation, training, evaluation, deployment, versioning, and monitoring using MLflow or equivalent.
  • Agentic AI Development is building multi-agent systems using frameworks such as LangChain, LangGraph, AutoGen, CrewAI, or similar; tool use, agent orchestration, and memory management.
  • Foundation Models and Model Serving fine-tuning, adapting, and deploying open-source and proprietary Foundation Models via REST APIs or model serving endpoints.
  • LLM Integration and Prompt Engineering, designing and optimising prompts, building LLM- powered applications, and integrating models such as OpenAI, Anthropic, Mistral, or Llama.
  • RAG Pipeline Building end-to-end Retrieval-Augmented Generation pipeline design, including document chunking, embedding generation, vector store integration, and response synthesis.

Good To Have Skills

  • Vector databases, such as Pinecone, Weaviate, Qdrant, or ChromaDB.
  • Azure AI Services: Azure OpenAI, Azure Machine Learning, Azure Cognitive Services.
  • AWS AI/ML Services: SageMaker, Bedrock, or Lambda for model deployment.
  • Orchestration frameworks: Apache Airflow or Prefect for ML pipeline scheduling.
  • AI/BI Dashboard development and data engineering experience.
  • Certifications: Microsoft Azure AI Engineer (AI-102), AWS ML Speciality, or equivalent.
  • Generative AI certification.

Technical Expertise

  • Area Technologies / Skills: Classical ML, Scikit-learn, XGBoost, MLflow feature engineering, model evaluation, and experiment tracking, Agentic AI LangChain, LangGraph, AutoGen, CrewAI multi-agent systems, tool use, orchestration.
  • LLM and Foundation: Models, OpenAI, Anthropic, Mistral, Llama fine-tuning, prompt engineering, model serving, RAG Pipelines Embeddings, vector stores (Pinecone, Weaviate, Qdrant, ChromaDB), retrieval and synthesis.
  • Languages: Python, SQL.
  • Good to Have Azure OpenAI, Azure ML, AWS SageMaker, AWS Bedrock, Airflow, AI/BI, Dashboards.

Other Requirements

  • 3-4 years of experience in ML/AI engineering roles.
  • Demonstrated experience building and deploying production ML pipelines and AI applications.
  • Strong communication skills, written and verbal; ability to articulate technical decisions clearly to both technical and non-technical audiences.
  • Strong problem-solving skills and meticulous attention to detail.
  • Ability to work independently and collaborate effectively with cross-functional teams.
  • Graduate degree (Pursuing candidates, dropouts, or 10th/12th pass-outs will not be considered).
  • Must be available to work from our Bengaluru office Monday to Friday, 9:30 AM to 6:30 PM.

This job was posted by Akshay Patil from Netscribes.

More Info

Job Type:
Permanent Job
Industry:
Other
Function:
Data Engineering
Employment Type:
Full time

About Company

Netscribes

Job ID: 148913309

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Last Updated: 09-06-2026 11:59:40 PM
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