Agentic AI Engineer

We have an urgent requirement for a Senior AI/ML Engineer to support end-to-end development, optimization, and deployment of advanced machine learning, deep learning, and generative AI solutions.

The SME will work closely with cross-functional engineering, data, and product teams to design scalable AI systems and drive adoption of agentic AI frameworks across the enterprise.

Key Responsibilities

AI/ML Engineering

• Design, build, and optimize ML and AI pipelines for predictive analytics, NLP, and generative AI workloads.
• Perform comprehensive Exploratory Data Analysis (EDA), statistical profiling, data mining, and visualization to extract actionable insights.
• Develop, train, validate, and tune ML models including supervised, unsupervised, tree-based, and ensemble techniques.
• Implement deep learning architectures (ANN, CNN, RNN, LSTM) using TensorFlow, PyTorch, and Keras.

Big Data & Data Engineering

• Architect and implement Big Data solutions using Hadoop, HDFS, MapReduce, Spark, PySpark, and Databricks.
• Build and maintain DataLake and cloud-native data processing solutions (AWS EMR, Redshift, Kinesis).
• Ensure performance, scalability, and reliability of distributed data pipelines.

NLP & Generative AI

• Build NLP pipelines using tokenization, embedding models, Word2Vec, n-grams, TF-IDF, CBoW, and transformer-based models.
• Work with large language models (BERT, GPT family, ELMo) for downstream NLP tasks.
• Integrate and deploy OpenAI models and embeddings (GPT-3.5 Turbo, GPT-4o, GPT-3o Reasoning, text-embedding-ada-002).

Agentic AI

• Implement autonomous AI workflows using agentic frameworks including LangChain, LangGraph, Model Context Protocol (MCP), Bedrock Agents, CrewAI, Helicone, and OpenAI Agents SDK.
• Design agents for task orchestration, tool interaction, reasoning chains, and multi-step problem solving.

Deployment & Productionisation

• Deploy ML/AI models into production environments ensuring high availability, scalability, and security.
• Collaborate with software engineers, cloud engineers, and product management to integrate AI solutions with enterprise applications.
• Establish CI/CD best practices for model lifecycle management, monitoring, and retraining.

Required Technical Skills

Analytical Tools & Techniques

• Strong expertise in EDA, statistical modelling, multivariate analysis.
• Visualization tools: Plotly, Matplotlib, Seaborn.

Big Data Technologies

• Hadoop, MapReduce, HDFS
• Spark, PySpark, Databricks
• DataLake architectures, AWS EMR, Redshift, Kinesis

Machine Learning

• Supervised: Naïve Bayes, Logistic Regression, SVM, Linear Regression, KNN
• Tree-Based: Decision Trees, Random Forest, Gradient Boosting, XGBoost
• Unsupervised: K-Means, DBSCAN, Hierarchical Clustering

Deep Learning

• ANN, CNN, RNN, LSTM
• TensorFlow (incl. Gradient Tape), PyTorch, Keras

NLP & Generative AI

• NLTK, Word2Vec, TF-IDF, Embeddings
• Transformers: BERT, ELMo
• OpenAI model suite and API integrations

Programming Languages

• Python (primary), R, C++, C#, Java
• SQL, Node.js, HTML

Libraries & Tools

• numpy, pandas, scipy, scikit-learn
• tensorflow, keras, nltk
• matplotlib, seaborn, plotly

Agentic AI Frameworks

• OpenAI Agents SDK
• Model Context Protocol (MCP)
• LangChain, LangGraph
• Bedrock Agents
• CrewAI, Helicone

Preferred Qualifications

• Experience deploying AI models on AWS, Azure, or GCP.
• Knowledge of MLOps best practices and CI/CD for model deployment.
• Prior exposure to enterprise-grade AI system design and architecture.
• Strong communication and stakeholder management skills.