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Responsibilities
We are looking for a highly skilled AI Engineer to help build and scale an automated machine learning platform that turns complex enterprise data into intelligent, production-ready solutions.
You will work across the full AI stack, from LLM development and RAG systems to MLOps, APIs, and cloud-native deployment, helping transform raw data into reliable, scalable AI services used in real-world business environments.
This role sits at the intersection of machine learning engineering, backend systems, and generative AI infrastructure, requiring both architectural thinking and strong hands-on execution.
What You Will Be Doing:
- You will be responsible for building and improving the core AI systems powering the platform.
- Design, train, and optimize ML models and LLM-based systems for predictive and generative use cases.
- Build and evolve Retrieval-Augmented Generation (RAG) pipelines using embeddings, vector databases, and prompt strategies.
- Develop scalable AI services with FastAPI, containerization, and cloud orchestration tools.
- Create and maintain automated data ingestion and transformation pipelines for ML-ready datasets.
- Deploy production-grade models with a focus on low latency, high availability, and scalability.
- Establish evaluation frameworks to monitor model accuracy, drift, and performance efficiency.
- Build secure APIs that connect AI capabilities to product interfaces and user-facing applications.
- Continuously improve system performance through optimization techniques and experimentation.
What We Are Looking For:
Core Experience:
- 5+ years building and deploying production machine learning systems.
- Strong background in taking models from research or notebooks into production environments.
- Hands-on experience across the full ML lifecycle and MLOps practices.
Technical Skills:
- Strong Python engineering skills (production-level, not just prototyping).
- Experience with ML frameworks like PyTorch, TensorFlow, or Scikit-learn.
- Deep understanding of LLMs and RAG architectures.
- Experience with tools like LangChain, LlamaIndex, or OpenAI APIs.
- Strong API development experience using FastAPI or Flask.
- Familiarity with SQL, NoSQL, and vector databases (Pinecone, Milvus, Weaviate, etc.).
- Experience with Docker, Kubernetes, or similar deployment tools.
- Cloud experience (AWS, GCP, or Azure).
Strong Plus:
- Exposure to AutoML systems or automated feature engineering pipelines.
- Experience with ML orchestration tools (MLflow, Kubeflow, Airflow, etc.).
- Understanding of distributed data processing (Spark, Dask).
- Experience with model tuning frameworks (Optuna, Ray Tune).
Ideal Candidate Profile:
- A product mindset with strong engineering discipline.
- Experience working in fast-moving startup environments.
- Ability to design scalable systems that evolve with new AI advancements.
- Strong mathematical foundation (statistics + linear algebra).
- A bias toward building reliable, production-grade systems over experimental-only work.
Day-to-Day Work:
- Building and improving ML/LLM pipelines powering enterprise use cases.
- Developing scalable inference services and APIs.
- Working on automated ML workflows (feature engineering
- training
- deployment).
- Improving model performance via tuning, optimization, and evaluation.
- Collaborating with data engineers to integrate diverse data sources.
- Writing production code and participating in peer reviews.
- Monitoring system health and model behavior in production.
Interview Process:
- Intro / Team Conversation
- background, collaboration fit, problem-solving style.
- Technical Assessment
- real-world coding or ML system challenge.
- Deep Technical Interview
- architecture, ML systems, and past experience deep dive.
- Final Round
- leadership discussion and long-term alignment.
Requirements (Must-Have):
- 5+ years in production ML/AI engineering.
- Strong experience with LLMs and RAG systems.
- Proven MLOps and deployment experience.
- Strong Python + backend engineering skills.
- Authorized to work in the U.S. (Citizen or Green Card holder).
Benefits:
- Health Insurance.
- Dental Insurance.
- Vision Insurance.
This job post is closed.