Senior AI/ML Engineer

Unifyed•Katy, TX

About The Position

We are looking for a hands-on Senior AI/ML Engineer who can own the full lifecycle of machine learning solutions - from problem definition and data modelling to training, deployment, monitoring, and continuous improvement. You should be comfortable working with messy real-world data, designing robust data models & features, building and training models, and shipping them to production with proper MLOps practices. You must also be aware of the current AI/ML landscape (LLMs, embeddings, vector search, modern tooling) and know when to use what.

Requirements

(6+ Years) Python Programming: Strong expertise in ML libraries (pandas, numpy, scikit-learn, PyTorch, TensorFlow)
SQL & Databases: Solid SQL skills and hands-on experience with relational and NoSQL data stores
Production ML: Demonstrated experience shipping end-to-end ML projects to production (not just notebooks / POCs)
ML Fundamentals: Deep understanding of supervised/unsupervised learning, evaluation metrics, overfitting, bias/variance, data leakage
Experiment tracking tools (MLflow, Weights & Biases)
Model versioning and packaging (Docker, virtualenv, Conda) CI/CD pipelines for ML services
Infrastructure as Code and containerization best practices
Proficiency with at least one major cloud platform: AWS: S3, EC2, SageMaker, Lambda, RDS, DynamoDB GCP: Cloud Storage, Compute Engine, Vertex AI, Firestore
Azure: Blob Storage, VMs, Azure ML, Cosmos DB API design (REST/GraphQL) and microservice architecture integration
Understanding of scalability, latency, and cost optimization
Exposure to LLMs & embeddings (OpenAI, HuggingFace, Anthropic, etc.) Familiarity with vector search & semantic search platforms (OpenSearch, Elasticsearch, Pinecone, Weaviate, pgvector)
Ability to make technical trade-offs between classical ML vs deep learning vs LLM-based approaches
Understanding of cost, latency, and accuracy considerations for each approach
Strong analytical thinking with ability to question requirements and propose better solutions
Independence: Can drive projects from ideation through production deployment with minimal guidance
Communication: Excellent at explaining technical trade-offs and complex concepts to both technical and non-technical stakeholders
Collaboration: Works well with cross-functional teams (product, data engineering, infrastructure, security

Responsibilities

Work with product / domain stakeholders to understand business problems and define ML use cases
Translate requirements into data & model design, success metrics, and clear technical plans
Own the full pipeline: data ingestion → cleaning → feature engineering → model training → evaluation → deployment → monitoring
Design and maintain data models / schemas optimized for analytics and ML training (batch & real time)
Perform exploratory data analysis (EDA) and feature engineering to improve signal quality and model performance
Work closely with data engineering to ensure reliable, well-documented datasets
Build, train, and tune models for tasks such as: prediction, classification, ranking, recommendations, anomaly detection, and NLP.
Use appropriate techniques (traditional ML, deep learning, embeddings, LLMs) based on the problem
Define and track offline and online metrics; run A/B tests or controlled experiments where applicable
Build reproducible training pipelines (e.g., using MLflow, Airflow, Kubeflow, or similar tools)
Package and deploy models as APIs / microservices or batch jobs, using containers and cloud services
Implement monitoring, alerting, and logging for model performance, data drift, and system health
Manage model versions, rollouts, and rollback strategies
Evaluate and integrate modern AI tools: vector databases, embedding models, LLM APIs, RAG architectures, etc. Ensure solutions follow security, privacy, and compliance best practices (e.g., PII handling, access control)
Write clear documentation for data flows, models, and services
Mentor junior engineers/data scientists and contribute to engineering standards and guidelines