AI Platform Engineer

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.

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.

Strong expertise in machine learning algorithms and techniques including: Classification Regression Clustering Recommendation Systems Forecasting Anomaly Detection

Classification

Regression

Clustering

Recommendation Systems

Forecasting

Anomaly Detection

Hands-on experience with: Scikit-Learn XGBoost

Scikit-Learn

XGBoost

LightGBM

CatBoost

Deep understanding of: Feature Engineering Model Evaluation Hyperparameter Tuning Cross Validation Explainable AI

Feature Engineering

Model Evaluation

Hyperparameter Tuning

Cross Validation

Explainable AI

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.

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

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

LangChain

LangGraph

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.

Working knowledge of AWS services commonly used for AI applications including: Amazon EKS SNS SQS Lambda S3 API Gateway

Amazon EKS

SNS

SQS

Lambda

S3

API Gateway

Ability to build scalable, cloud-native AI solutions.

Experience with Docker for packaging and deploying AI applications.

Understanding of container-based application development and deployment.

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

Experience processing large-scale datasets using Spark.

Understanding of distributed data processing and big data workloads.

Experience with CrewAI or similar multi-agent frameworks.

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

Experience working with one or more of:

OpenSearch

pgvector

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

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

Exposure to GraphRAG architectures for improving reasoning and explainability.

Understanding of event-driven design patterns.

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