Key Skills: AI/ML Engineer, Machine Learning Engineer, Data Scientist, AI Engineer, Applied Scientist.

- Python, TensorFlow, PyTorch, Scikit-learn, deep learning, NLP, computer vision, SQL, PostgreSQL, ETL, data pipelines, cloud (AWS, GCP, Azure), model deployment.

- Agentic AI, multi-agent, tool calling, LangChain, Google ADK, RAG pipelines.

- Good to have: Hadoop, Spark.

Key Responsibilities:

- Machine Learning Model Development: Research, design, and implement advanced machine learning algorithms and models to address business challenges.

- Data Engineering: Extract, transform, and load (ETL) large datasets from various sources into a Postgres database.

- Model Deployment: Deploy machine learning models into production environments, ensuring optimal performance and scalability.

- AI Integration: Integrate AI capabilities into existing systems and applications.

- Agentic AI / GenAI: Build and integrate agentic AI and RAG-based applications using Python and frameworks such as LangChain and Google ADK, including tool calling and multi-agent workflows.

- Document AI / IDP: Build and tune models for document classification, OCR post-processing, layout-aware key-value and table extraction, and LLM-based extraction, with confidence scoring and a human-in-the-loop feedback loop.

- Team Leadership: Mentor and guide junior team members in machine learning and data science practices.

- Problem Solving: Analyze complex business problems and develop innovative solutions using machine learning techniques.

- Collaboration: Work closely with cross-functional teams to understand business requirements and translate them into technical solutions

Required Skill Set:

Technical Skills:

- Machine Learning: Strong proficiency in machine learning algorithms (regression,



classification, clustering, deep learning) and their implementation using Python libraries (TensorFlow, PyTorch, Scikit-learn).

- AI: Understanding of AI concepts, including natural language processing (NLP), computer vision, and reinforcement learning.

- Generative AI and Agentic AI: Hands-on experience with RAG, LangChain, Google ADK, and building agentic workflows (tool calling, multi-agent orchestration) in Python.

- Programming: Expert-level Python programming skills, including data manipulation, statistical analysis, and model development.

- Database: Proficiency in SQL and experience with PostgreSQL for data storage and retrieval.

- Cloud Platforms: Familiarity with cloud platforms (AWS, GCP, Azure) for deploying and managing machine learning workloads.

- Data Engineering: Experience with data pipelines, ETL processes, and data preprocessing techniques.

- Big Data: Knowledge of big data technologies (Hadoop, Spark) is a plus.

- IDP (positive to have): Experience with OCR engines or cloud OCR services, document understanding models, LLM or vision-language based extraction, and extraction accuracy evaluation.

Soft Skills:

- Problem Solving: Ability to analyze complex problems and develop effective solutions.

- Communication: Excellent communication skills to explain technical concepts to both technical and non-technical audiences.

- Collaboration: Strong teamwork and interpersonal skills to work effectively with cross-functional teams.

- Leadership: Ability to mentor and guide junior team members.

- Business Acumen: Understanding of business objectives and how machine learning can drive business value.

- Continuous Learning: Passion for staying updated with the latest advancements in machine learning and AI.

📌 AI/ML Engineer - Bangalore and Pune (Bengaluru)
🏢 EdgeVerve Systems
📍 Bengaluru