hackajob
Senior/Lead Machine Learning Engineer
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Job Role
hackajob is collaborating with Version 1 to connect them with exceptional professionals for this role.
Design, build, and deploy machine learning solutions that solve real business problems, moving from prototype to production.
- Apply traditional ML (e.g., regression/classification/clustering) and deep learning techniques where appropriate, selecting models based on evidence and constraints.
- Demonstrate strong ML fundamentals, including the mathematics behind models (probability, statistics, optimisation, linear algebra), and explain trade-offs clearly.
- Develop and deploy ML and data science solutions from proof of concept to production.
- Perform data exploration, feature engineering, and model development on large datasets.
- Track experiments, metrics, and model versions (e.g., MLflow).
- Collaborate with data engineers and AI engineers to integrate models into platforms.
- Continuously improve models based on performance, feedback, and data drift.
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Qualifications
Required Skills & Experience
- 5+ years in applied machine learning and deep learning roles.
- Strong grounding in core ML concepts and their mathematical basis:
- Probability & statistics, hypothesis testing, bias/variance, regularisation.
- Optimisation (e.g., gradient-based methods), loss functions, evaluation metrics.
- Linear algebra fundamentals used in ML (vectors/matrices, decompositions at a practical level).
- Solid practical experience with traditional ML modelling (feature engineering, model selection, validation, and error analysis).
- Demonstrable exposure to deep learning (architectures, training dynamics, evaluation), beyond “surface-level” familiarity.
- Proven ability to build good quality software, not just models—clean code, testing, debugging, and maintainable design.
- Strong programming skills (typically Python; additional languages a plus) and experience integrating ML into production systems.
- A clear problem-solving mindset: structured thought process, ability to reason through ambiguous requirements, and iterate effectively.
- Hands-on experience delivering ML solutions end-to-end, including prototyping, validation, and production/operations.
- Experience with Databricks and Spark.
- Hands-on use of MLflow or similar model lifecycle and MLOps frameworks.
- Experience with deep learning frameworks (e.g., PyTorch).
- Practical experience with GenAI / LLMs.
- Exposure to AWS Bedrock & AWS SageMaker.
- Strong SQL and data analysis skills.
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Nice to Have
- Experience in regulated or security conscious environments.
- Familiarity with model governance, monitoring, and managing model performance over time.
- Exposure to production model deployment patterns.
- Familiarity with Responsible AI and model governance.
- Client facing or consulting experience.
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