Qodea
Information Technology and Services
SeniorMachineLearningEngineer
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“Senior Machine Learning Engineer at Qodea. Skills: Machine Learning, Python, ML lifecycle management, production-grade ML systems. Lead the algorithm selection, design, and prototyping of machine learning models to solve complex business problems, including recommendation, personalization, and predictive analytics. Apply expertise in statistical modeling and machine learning to perform deep data analysis, guide crucial feature selection, and identify opportunities for product improvement”
Industry & Context.
solve complex business problems; solving problems that don't even have answers yet; solve the hardest problems
spend time on site (at our offices or a client location) for collaboration sessions, customer meetings, and internal workshops
What They're Looking For.
Must Have
Hands-on experience designing and deploying production-grade machine learning systems, foundational knowledge of various machine learning algorithms and a proven ability to select the appropriate methodology, avoiding a one-size-fits-all approach, Proven experience in areas such as recommendation systems, personalization, natural language processing (NLP), or semantic search, Expert-level programming skills in Python, with deep, hands-on experience using data science and ML libraries such as Pandas, Scikit-learn, TensorFlow, or PyTorch, Experience with data storage technologies (e.g., SQL, NoSQL, Key-value) and their scaling characteristics, Experience with large-scale data processing technologies (e.g., Spark, Beam, Flink) and associated patterns (Batch vs. Stream), with a deep understanding of when to use them, Experience using cloud platforms (e.g., GCP) at scale, Experience deploying ML-based solutions at scale using cloud-native services, Excellent communication and collaboration skills, with the ability to thrive in a fast-paced, cross-functional team environment
Nice to Have
Knowledge of various machine learning algorithms and a proven ability to select the appropriate methodology, avoiding a one-size-fits-all approach, Experience in areas such as recommendation systems, personalization, natural language processing (NLP), or semantic search, Experience using cloud platforms (e.g., GCP) at scale, Experience deploying ML-based solutions at scale using cloud-native services, Excellent communication and collaboration skills, with the ability to thrive in a fast-paced, cross-functional team environment
What You'll Do.
Lead the algorithm selection
and prototyping of machine learning models to solve complex business problems
including recommendation
and predictive analytics
Apply expertise in statistical modeling and machine learning to perform deep data analysis
guide crucial feature selection
and identify opportunities for product improvement
Own the full ML lifecycle
from breaking down discrete steps of a pipeline (e.g.
with a DAG) to analyzing model implementations and improving their robustness in the wild
Implement and manage robust model observability
and optimization processes to ensure sustained performance and accuracy post-deployment
Develop and maintain data pipelines to process and prepare data for model training and evaluation
Design and conduct A tests to evaluate model performance and its impact on key business metrics
Collaborate closely with product managers and engineers to define problems and deliver effective AI-driven solutions
Mentor other team members
champion best practices in machine learning engineering
and stay current with the latest advancements in the field
How You'll Work.
Team & Collaboration
Collaborate closely with product managers and engineers to define problems and deliver effective AI-driven solutions; Mentor other team members, champion best practices in machine learning engineering, and stay current with the latest advancements in the field; Excellent communication and collaboration skills, with the ability to thrive in a fast-paced, cross-functional team environment; spend time on site (at our offices or a client location) for collaboration sessions, customer meetings, and internal workshops
Communication Scope
Excellent communication and collaboration skills, with the ability to thrive in a fast-paced, cross-functional team environment
Process & Methodology
breaking down discrete steps of a pipeline (e.g., with a DAG)
Full Job Description
### Work where work matters. Elevate your career at Qodea, where innovation isn't just a buzzword, it's in our DNA. We are a global technology group built for what's next, offering high calibre professionals the platform for high stakes work, the kind of work that defines an entire career. When you join us, you're not just taking on projects, you're solving problems that don't even have answers yet. You will join the exclusive roster of talent that global leaders, including Google, Snap, Diageo, PayPal, and Jaguar Land Rover call when deadlines seem impossible, when others have already tried and failed, and when the solution absolutely has to work. Forget routine consultancy. You will operate where technology, design, and human behaviour meet to deliver tangible outcomes, fast. This is work that leaves a mark, work you’ll be proud to tell your friends about. Qodea is built for what’s next. An environment where your skills will evolve at the frontier of innovation and AI, ensuring continuous growth and development. We are looking for a **Senior Machine Learning Engineer** to be responsible for the end-to-end lifecycle of machine learning models that power core product features. You will design, build, and deploy innovative ML solutions, directly impacting the user experience through personalization, recommendations, and intelligent systems. We look for people who embody: **Innovation** to solve the hardest problems. **Accountability** for every result. **Integrity** always. ### About The Role * Lead the algorithm selection, design, and prototyping of machine learning models to solve complex business problems, including recommendation, personalization, and predictive analytics. * Apply your expertise in statistical modeling and machine learning to perform deep data analysis, guide crucial feature selection, and identify opportunities for product improvement. * Own the full ML lifecycle, from breaking down discrete steps of a pipeline (e.g., with a DAG) to analyzing
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