AIML Engineer- Immediate- Bangalore

Experience: 4+ Years
Location: Bangalore
Joining: Immediate Joiners Preferred

Job Summary

We are looking for an experienced AI/ML Engineer with hands-on expertise in Agentic AI, AI orchestration, applied machine learning, optimization, Databricks integration, LLMOps, and Scientific Machine Learning. The ideal candidate should have experience building intelligent AI solutions, optimizing ML models, developing scalable data pipelines, and deploying and monitoring AI/ML applications in production environments.

Key Responsibilities

• Design and develop Agentic AI applications using multi-agent architectures, LLMs, and orchestration frameworks.
• Build and manage AI workflows involving tool calling, workflow routing, memory management, and error handling.
• Develop, train, evaluate, and optimize machine learning models for real-world business problems.
• Implement hyperparameter tuning, feature engineering, model optimization, and performance evaluation techniques.
• Develop scalable data processing and ML pipelines using Databricks, Apache Spark, PySpark, and Delta Lake.
• Integrate AI/ML models with enterprise data platforms, cloud services, APIs, and production applications.
• Implement LLMOps practices, including experiment tracking, model versioning, evaluation, deployment, monitoring, and CI/CD.
• Apply Scientific ML techniques to solve complex problems involving mathematical modeling, numerical simulations, and physics-informed learning.
• Monitor model performance, detect data drift, troubleshoot production issues, and continuously improve model accuracy and efficiency.
• Collaborate with data engineers, software developers, and cross-functional teams to deliver scalable AI/ML solutions.

Required Technical Skills

1. Agentic AI & Orchestration

• Hands-on experience with Agentic AI and multi-agent systems.
• Experience with LangChain, LangGraph, or similar AI orchestration frameworks.
• Knowledge of LLM tool calling, function calling, workflow orchestration, and agent communication.

2. Applied ML & Optimization

• Strong Python programming skills.
• Experience with machine learning algorithms, model training, feature engineering, and evaluation.
• Knowledge of Scikit-learn, PyTorch, or TensorFlow.
• Experience with hyperparameter tuning, optimization algorithms, and model performance improvement.

3. Databricks & Integration

• Experience with Databricks, Apache Spark, and PySpark.
• Knowledge of Delta Lake, data engineering workflows, and scalable data processing.
• Experience integrating ML models with data pipelines, APIs, and cloud platforms.

4. LLMOps & Deployment

• Experience deploying and managing LLM-based applications in production.
• Knowledge of MLflow, experiment tracking, model versioning, and model registry.
• Experience with LLM evaluation, performance monitoring, prompt management, and CI/CD pipelines.
• Understanding of model serving, latency optimization, and inference cost management.

5. Scientific Machine Learning

• Knowledge of Scientific ML, numerical methods, and mathematical modeling.
• Familiarity with Physics-Informed Neural Networks (PINNs), differential equations, and scientific computing.
• Understanding of simulation modeling, surrogate models, and uncertainty quantification.
• Experience with SciPy, NumPy, PyTorch, or TensorFlow for scientific and numerical applications.

Good-to-Have Skills

• Experience with AWS, Azure, or GCP.
• Knowledge of Docker, Kubernetes, and GPU-based computing.
• Exposure to MLOps automation, model governance, and production monitoring.
• Experience building enterprise-grade Generative AI and LLM applications.

Candidate Profile

• 4+ years of relevant experience in AI/ML engineering.
• Strong hands-on development and problem-solving skills.
• Experience building, integrating, and deploying AI/ML solutions.
• Ability to work across AI orchestration, machine learning, data platforms, and production deployment.
• Immediate joiners preferred.