AI/Machine Learning Engineer
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About the Role
We are looking for a hands-on AI/ML Engineer who can take an idea from concept to deployment. You will be responsible for designing, developing, training, testing, and integrating machine learning models into core systems.
This role requires a versatile professional skilled in data science, software engineering, and MLOps to deliver end-to-end AI solutions that support business objectives.
Key Responsibilities
Data & Model Development
Collect, clean, and preprocess data for training and testing.
Design and develop machine learning models (classical ML or deep learning).
Train, tune, and validate models using real-world datasets.
Conduct performance testing and error analysis.
System Integration
Develop APIs or services to integrate the model into the main product or platform.
Collaborate with backend and frontend developers to embed AI functionality.
Ensure efficient inference and scalability in production environments.
MLOps & Deployment
Package and deploy models (e.g., via Docker, FastAPI, or cloud ML services).
Set up monitoring for model accuracy, drift, and system performance.
Maintain version control for models and data pipelines.
Collaboration & Documentation
Work closely with the Product Manager to translate business goals into technical solutions.
Document models, datasets, and architecture decisions.
Communicate findings and results clearly to non-technical stakeholders.
Qualifications
Bachelor’s or Master’s degree in Computer Science, Data Science, AI/ML, or a related field.
3+ years of experience in applied machine learning or AI engineering.
Strong proficiency in Python and frameworks such as TensorFlow, PyTorch, or Scikit-learn.
Experience building and deploying APIs (e.g., FastAPI, Flask, Node).
Familiarity with cloud services (AWS, GCP, Azure) or containerization (Docker, Kubernetes).
Practical knowledge of data pipelines (ETL, data versioning, labeling tools).
Strong understanding of model evaluation and performance tuning.
Bonus Skills
Experience with LLMs, prompt engineering, or LangChain.
Familiarity with vector databases (Pinecone, FAISS, Milvus).
Exposure to frontend frameworks (React, Vue) for prototyping.
Understanding of data security and compliance in regulated domains (e.g., fintech, healthcare).
What We Offer
Opportunity to develop and deploy AI solutions end-to-end.
Autonomy in technical design and implementation.
Direct impact on key business initiatives.
Collaborative environment with continuous learning opportunities.