Job Description & How to Apply Below
AI/ML Engineer – Generative & Agentic AI
Job Title:
AI/ML Engineer
Company:
Express Analytics (EA)
Location:
Remote
Employment Type:
Full‑time
Experience:
1-3 Years
Salary:
Competitive, up to market standards
About Express Analytics
Express Analytics builds AI‑powered marketing and customer analytics solutions for global clients, combining data engineering, ML, and generative AI to drive measurable business outcomes. You’ll work on production systems that power agentic marketing workflows, customer analytics products, and domain‑specific GenAI applications.
Role overview
As an AI/ML Engineer, you will design, build, and ship machine learning and generative AI solutions end‑to‑end—from problem definition and modeling through to deployment, monitoring, and iteration. You will work closely with product, data, and engineering teams to turn business requirements into robust models and agentic workflows that run reliably in production.
What you’ll do
Design and build ML & GenAI pipelines
● Own end‑to‑end pipelines for recommendation, forecasting, customer analytics, and generative/NLP workloads (including retrieval, chunking, and summarization).
● Select and implement appropriate models (classical ML, deep learning, and LLM‑based approaches) based on use‑case constraints.
Agentic / LLM systems engineering
● Implement multi‑step and multi‑agent workflows using LLM frameworks (e.g., Lang Chain, Lang Graph or similar) with tools, memory, and external API integrations.
● Build and refine RAG pipelines: document preprocessing, embeddings, retrieval strategies, evaluation, and guardrails.
Productization & backend integration
● Productionize models behind APIs and microservices using Python (FastAPI or similar) and integrate with existing product backends and frontends.
● Implement CI/CD for ML services, containerize workloads (Docker), and collaborate on cloud deployment (e.g., GCP/AWS/Azure).
Experimentation, evaluation, and optimization
● Define success metrics, design experiments, and run systematic evaluations for both discriminative models and LLM‑based systems.
● Optimize for latency, cost, and reliability; profile and tune models, prompts, and infrastructure.
Data and analytics collaboration
● Work with data engineers to ensure high‑quality feature and event data, and with analytics teams to translate insights into models and agents that drive impact.
Documentation and technical leadership
● Maintain clear documentation of architectures, experiments, and decisions.
● Mentor interns/junior members on ML/LLM best practices and engineering hygiene where relevant.
What we’re looking for
Experience
● 1-3 years of hands-on experience building and deploying ML models or LLM‑based systems in production (can include strong startup or product‑focused experience).
Core technical skills
● Strong proficiency in Python and ML stack: pandas, Num Py, scikit‑learn; experience with at least one deep learning framework (PyTorch or Tensor Flow).
● Solid understanding of ML fundamentals: supervised/unsupervised learning, evaluation metrics, feature engineering, model validation.
● Practical experience with LLMs (OpenAI, Anthropic, open‑source models etc.) including prompt design, fine‑tuning or instruction tuning, and/or RAG.
Agentic & GenAI skills (nice to have but highly valued)
● Experience with LLM orchestration frameworks (Lang Chain, Lang Graph, CrewAI, or similar) to build tools‑using or multi‑agent systems.
● Experience designing retrieval systems: vector databases, embeddings, chunking strategies, and evaluation of generative outputs.
Software engineering & data skills
● Experience building APIs/microservices (FastAPI/Django/Flask or Node) and integrating with frontends or partner systems.
● Familiarity with SQL, basic data modeling, and working with warehouses or data lakes.
● Experience with Git, Docker, and CI/CD pipelines; familiarity with cloud services (GCP/AWS/Azure).
Nice to have
● Experience in marketing tech, customer analytics (e.g., Google Ads, attribution, MMM, LTV modeling).
● Experience with analytics/BI tooling and experimentation (dashboards, A/B tests).
● Prior work on voice/agents, conversational AI, or domain‑specific document AI.
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