Senior Technical Consultant

Job Summary

Job Title: AI Engineer (Machine Learning & Generative AI)

We are looking for an experienced AI Engineer with 4-8 years of experience in Machine Learning, Artificial Intelligence, and Generative AI. The ideal candidate will design, develop, and deploy scalable AI solutions across traditional machine learning and GenAI use cases.

Qualifications

Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Statistics, Engineering, or related field.

4-8 years of experience in AI/ML or Data Science roles.

Strong Python programming and AWS experience.

Key Responsibilities

Build end-to-end ML solutions for classification, regression, recommendation, forecasting, clustering, anomaly detection, and optimization problems.

Perform feature engineering, model experimentation, hyperparameter tuning, and model evaluation.

Design and execute A/B testing and model validation strategies.

Develop explainable and auditable ML models for business-critical applications.

Monitor model performance and support retraining initiatives.

Design and develop LLM-powered applications.

Build RAG systems using vector databases and enterprise knowledge repositories.

Develop AI copilots, intelligent assistants, and agentic workflows.

Implement prompt engineering, prompt optimization, evaluation frameworks, and guardrails.

Develop production-grade AI services, APIs, SDKs, and microservices.

Design scalable REST APIs and event-driven architectures.

Apply design patterns, SOLID principles, and clean coding standards.

Work with Docker, EKS, SNS, SQS, and other AWS services.

Key Responsibilities

Must-Have Skills

Python

Strong hands-on experience with Python for developing production-grade AI and machine learning applications.

Ability to write clean, maintainable, and reusable code following software engineering best practices.

Experience developing APIs, automation frameworks, and AI services using Python.

SQL & Data Analysis

Strong proficiency in SQL for data extraction, transformation, and analysis.

Experience working with large-scale structured and semi-structured datasets.

Ability to perform exploratory data analysis and derive business insights from data.

Machine Learning

Strong expertise in machine learning algorithms and techniques including:

Classification

Regression

Clustering

Recommendation Systems

Forecasting

Anomaly Detection

Hands-on experience with:

Scikit-Learn

XGBoost

LightGBM

CatBoost

Deep understanding of:

Feature Engineering

Model Evaluation

Hyperparameter Tuning

Cross Validation

Explainable AI

Deep Learning & NLP

Practical experience developing NLP solutions using TensorFlow or PyTorch.

Understanding of transformers, embeddings, and modern NLP techniques.

Experience with text classification, semantic search, summarization, information extraction, and conversational AI use cases.

Generative AI

Hands-on experience building enterprise-grade GenAI applications.

Strong understanding of:

Large Language Models (LLMs)

Prompt Engineering

Retrieval-Augmented Generation (RAG)

Agentic AI Workflows

Structured Output Generation

Evaluation Frameworks

Experience using frameworks such as:

LangChain

LangGraph

API & Software Engineering

Experience designing and developing RESTful APIs and microservices.

Strong understanding of design patterns, object-oriented programming, and SOLID principles.

Experience creating reusable AI components, SDKs, and shared libraries.

AWS Cloud Technologies

Working knowledge of AWS services commonly used for AI applications including:

Amazon EKS

SNS

SQS

Lambda

S3

API Gateway

Ability to build scalable, cloud-native AI solutions.

Containerization

Experience with Docker for packaging and deploying AI applications.

Understanding of container-based application development and deployment.

Version Control

Strong experience with Git and collaborative development workflows including code reviews, branching strategies, and release management.

Skill Requirements

Good-to-Have Skills

Apache Spark

Experience processing large-scale datasets using Spark.

Understanding of distributed data processing and big data workloads.

AI Agent Frameworks

Experience with CrewAI or similar multi-agent frameworks.

Understanding of agent orchestration, tool usage, memory management, and autonomous workflows.

Vector Databases

Experience working with one or more of:

OpenSearch

pgvector

Knowledge of embeddings, vector search, and semantic retrieval techniques.

Knowledge Graphs & GraphRAG

Understanding of knowledge graph concepts and graph-based retrieval techniques.

Exposure to GraphRAG architectures for improving reasoning and explainability.

Event-Driven Architecture

Understanding of event-driven design patterns.

Experience integrating SNS, SQS, Kafka, or similar messaging technologies into AI solutions."

Other Requirements