krtrimaiq cognitive solutions
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Machine Learning (ML)Job description
We are looking for a Data Scientist with strong AI/ML engineering skills to join our high-impact team at KrtrimaIQ Cognitive Solutions. This is not a notebook-only role — you must have production-grade experience deploying and scaling AI/ML models in cloud environments, especially GCP, AWS, or Azure.
This role involves building, training, deploying, and maintaining ML models at scale, integrating them with business applications. Basic model prototyping won't qualify — we’re seeking hands-on expertise in building scalable machine learning pipelines.
Key Responsibilities
Design, train, test, and deploy end-to-end ML models on GCP (or AWS/Azure) to support product innovation and intelligent automation.
Implement GenAI use cases using LLMs
Perform complex data mining and apply statistical algorithms and ML techniques to derive actionable insights from large datasets.
Drive the development of scalable frameworks for automated insight generation, predictive modeling, and recommendation systems.
Work on impactful AI/ML use cases in Search & Personalization, SEO Optimization, Marketing Analytics, Supply Chain Forecasting, and Customer Experience.
Implement real-time model deployment and monitoring using tools like Kubeflow, Vertex AI, Airflow, PySpark, etc.
Collaborate with business and engineering teams to frame problems, identify data sources, build pipelines, and ensure production-readiness.
Maintain deep expertise in cloud ML architecture, model scalability, and performance tuning.
Stay up to date with AI trends, LLM integration, and modern practices in machine learning and deep learning.
Technical Skills Required Core ML & AI Skills (Must-Have):
Strong hands-on ML engineering (70% of the role) — supervised/unsupervised learning, clustering, regression, optimization.
Experience with real-world model deployment and scaling, not just notebooks or prototypes.
Good understanding of ML Ops, model lifecycle, and pipeline orchestration.
Strong with Python 3, Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, Seaborn, Matplotlib, etc.
SQL proficiency and experience querying large datasets.
Deep understanding of linear algebra, probability/statistics, Big-O, and scientific experimentation.
Cloud experience in GCP (preferred), AWS, or Azure.
Cloud & Big Data Stack
Hands-on experience with:
GCP tools – Vertex AI, Kubeflow, BigQuery, GCS
Or equivalent AWS/Azure ML stacks
Familiar with Airflow, PySpark, or other pipeline orchestration tools.
Experience reading/writing data from/to cloud services.
Qualifications
Bachelor's/Master’s/Ph.D. in Computer Science, Mathematics, Engineering, Data Science, Statistics, or related quantitative field.
4+ years of experience in data analytics and machine learning roles.
2+ years of experience in Python or similar programming languages (Java, Scala, Rust).
Must have experience deploying and scaling ML models in production.
Nice to Have
Experience with LLM fine-tuning, Graph Algorithms, or custom deep learning architectures.
Background in academic research to production applications.
Building APIs and monitoring production ML models.
Familiarity with advanced math – Graph Theory, PDEs, Optimization Theory.
Communication & Collaboration
Strong ability to explain complex models and insights to both technical and non-technical stakeholders.
Ask the right questions, clarify objectives, and align analytics with business goals.
Comfortable working cross-functionally in agile and collaborative teams.
Important Note:
This is a Data Science-heavy role — 70% of responsibilities involve building, training, deploying, and scaling AI/ML models.
Cloud experience is mandatory (GCP preferred, AWS/Azure acceptable).
Only candidates with hands-on experience in deploying ML models into production (not just notebooks) will be considered.
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