Agentic AI Engineer

Key Responsibilities

Agentic AI Development

Design and implement autonomous, goal-oriented agents AI agents capable of decision-making, planning, and task execution with minimal human intervention

Develop multi-agent systems that can collaborate, negotiate, and coordinate to solve complex business problems.

Build AI agents with reasoning capabilities using scalable and robust tools on appropriate agentic AI platforms

Build monitoring and debugging tools for non-deterministic agent behaviour/ Guardrai1ls to prevent harmful actions / Comprehensive testing frameworks / Roll back mechanisms / fault-tolerant agentic systems with robust error handling and recovery mechanisms

Optimize agent performance for real-time decision making in high-stakes financial and telecom environments

Advanced AI Architecture

Architect compound AI systems that combine multiple AI models, tools, and data sources, domain-specific knowledge bases with memory systems and persistent state management; interacting with external APIs and databases

Develop multi-modal agents capable of processing text, voice, and structured data

Cross-Functional Partnership

Collaborate closely with product managers to translate complex business requirements into agentic AI solutions

Partner with domain experts in telecom and fintech to ensure AI agents understand industry-specific workflows and regulations

Work with UX/UI teams to design intuitive interfaces for human-agent collaboration

Engage with compliance and security teams to ensure agentic systems meet regulatory requirements

Technical Leadership

Mentor junior engineers and data scientists on ML best practices

Lead technical discussions and architecture decisions for ML projects

Collaborate with cross-functional teams including product, engineering, and data t

Conduct code reviews and establish ML engineering standards

Present findings and recommendations to technical and non-technical stakeholders

Required Qualifications

Education & Experience

Bachelor's or Master's degree in Computer Science, Machine Learning, Statistics, Mathematics, or related field

8+ years of experience in machine learning, feature engineering, statistical analysis of data, deploying models to prod environment

1+ years of solid hands-on experience Agentic AI and deployment

Technical Skills

Programming Languages: Proficiency in Python, with experience in R, Scala, or Java preferred

ML Frameworks: Expert-level experience with TensorFlow, PyTorch, scikit-learn

Cloud Platforms: Experience with AWS, GCP, or Azure ML services

Big Data Tools: Familiarity with Spark, Hadoop, Kafka, or similar distributed systems

Containerization: Experience with Docker, Kubernetes for ML model deployment

Databases: Vector Databases, Proficiency in SQL and experience with NoSQL databases (MongoDB, Cassandra)

Version Control: Git, MLflow, DVC, or similar ML versioning tools

ML Expertise

Deep understanding of Traditional and Generational ML Models, hyperparameter tuning, feature engineering, EDA, quality measurement, Model convergence, regularization

Understanding of model interpretability and explainability techniques

Software Engineering

Strong software engineering principles and design patterns

Experience with microservices architecture and API development

Knowledge of software testing, including unit testing for ML code

Familiarity with monitoring, logging, and debugging distributed systems

Understanding of security best practices for ML systems

Preferred Qualifications

Experience with large language models (LLMs) and generative AI

Knowledge of federated learning, edge AI, or mobile ML deployment

Experience with real-time inference and low-latency model serving

Contributions to open-source ML projects or research publications

Telecommunications domain knowledge and experience with BSS/OSS systems

Leadership or technical mentoring experience