Senior AI/ML Engineer
Data Scientist
If you have strong experience with LLMs, Generative AI systems, data pipelines, or applied machine learning, we want to speak with you.
🔍 What You Will Do
----------------------------------------------------------------------------------------
LLM & Generative AI Development
- Build, fine-tune, and evaluate LLMs for chatbots, agents, summarization, classification, and automation workflows
- Design prompt engineering, prompt chaining, and RAG (retrieval-augmented generation) pipelines
- Implement embeddings, vector search, and hybrid search workflows
- Conduct model evaluation using quantitative and qualitative metrics
- Prototype and ship applied AI solutions that solve real business problems
Machine Learning & Data Science
- Perform data exploration, feature engineering, hypothesis testing, and modeling
- Build predictive and classification models using modern ML techniques (transformers & classical ML)
- Create datasets for model training, fine-tuning, and evaluation
- Build dashboards, insights, and analytics to support product decisions
Data Engineering & ML Infrastructure
- Build scalable ETL/ELT pipelines for ingestion, transformation, and model readiness
- Integrate data from APIs, databases, cloud services, and unstructured sources
- Prepare vectorization pipelines for RAG and LLM applications
- Support deployment of AI/ML models using APIs, containers, and microservices
- Develop monitoring, logging, CI/CD and automated workflows for stable production systems
Cloud, Storage & Performance
- Work with Azure/AWS/GCP for storage, compute, and networking
- Manage SQL/NoSQL databases, warehouses, and data lakes
- Optimize pipelines, improve reliability, and ensure scalability
- Maintain performance dashboards and observability tools
⭐ Ideal Candidate Profile
----------------------------------------------------------------------------------------
You may fit one (or more) of these backgrounds:
Core Technical Skills
- Strong Python programming skills
- Strong SQL skills (data modeling, queries, optimization)
- Experience with cloud platforms (
Azure preferred
, AWS/GCP also welcome) - Experience with APIs, data ingestion, logs, and structured + unstructured data
- Experience supporting large-scale AI/ML workloads and production systems
LLMs & Generative AI
- Hands-on experience with
OpenAI
, Llama
, HuggingFace
, Anthropic
, or similar LLM providers - Strong understanding of transformers, NLP fundamentals, embeddings, and tokenization
- Experience with
RAG
, vector search, and embeddings - Familiarity with vector databases (
Pinecone
, FAISS
, Chroma
, etc.) - Ability to design, evaluate, and optimize prompts and LLM workflows
- Experience building chatbots, agents, summarizers, classifiers, or GenAI automation tools
Machine Learning & Statistical Foundations
- Solid grounding in
statistics
, probability
, and machine learning
concepts - Experience building and validating ML models (traditional ML or deep learning)
- Ability to evaluate model performance using quantitative and qualitative metrics
- Experience preparing datasets for training, fine-tuning, and evaluation
Data Engineering & Pipelines
- Experience building and maintaining
ETL/ELT
pipelines - Strong data modeling, schema design, and data quality practices
- Experience integrating data from APIs, DBs, cloud systems, and external sources
- Experience with pipelines for embeddings, vectorization, and model preparation
- Familiarity with streaming/real-time systems (Kafka, EventHub) is a plus
ML Ops & Infrastructure
- Familiarity with ML Ops tooling and workflows (CI/CD, testing, monitoring)
- Experience deploying models via REST APIs, Docker, containers, or microservices
- Ability to design stable, scalable, and reliable AI/ML infrastructure
- Experience with GPUs, compute optimization, or distributed systems is a plus
End-to-End AI/ML Engineering
- Comfortable working across both
model development
and data/infrastructure
- Ability to design and deliver end-to-end solutions from ingestion → processing → model → deployment
- Strong problem solver who can translate business needs into technical architectures
🎯 Required Skills
----------------------------------------------------------------------------------------
- 3–7+ years of experience in Data Science, Data Engineering, ML Engineering, or AI Engineering
- Strong Python skills
- Experience with LLMs, Generative AI, or ML modeling
- Understanding of cloud environments (Azure/AWS/GCP)
- Experience with SQL, APIs, and data modeling
- Ability to turn business problems into technical architectures
✨ Nice-to-Have
----------------------------------------------------------------------------------------
- Experience fine-tuning LLMs (LoRA, QLoRA)
- Experience deploying models on GPUs
- Experience with Kubernetes, Docker, Terraform/Bicep
- Familiarity with streaming systems (Kafka, EventHub)
- Experience with multi-agent workflows
📨 How to Apply
----------------------------------------------------------------------------------------
If you're passionate about building real AI systems — not just prototypes — and want to work on impactful, production-grade solutions, we’d love to hear from you. Please share your resume at thowzif.abdullah@resunconsulting.com or apply here with your resume.
