Technical Lead
Confirmed on the employer's own hiring board on Oct 28, 2026. First seen by Alion on Oct 28, 2026.
Job Summary
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
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."