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Key Responsibilities:
- Architect and implement scalable AI/ML solutions, including robust MLOps pipelines for CI/CD, model monitoring, and governance.
- Lead the development of Generative AI and NLP applications such as text summarization, conversational AI, and entity recognition.
- Design and optimize large-scale data pipelines using PySpark and SQL.
- Drive scalable ML model development, deployment, and performance tuning.
- Collaborate with cross-functional teams to align AI efforts with strategic business goals.
- Mentor and guide junior team members on best practices and technical direction.
Required Qualifications:
- Bachelor's degree with 10–12 years of experience in Data, AI, and Machine Learning domains.
- Proficient in Python (Pandas, NumPy, PyTorch, TensorFlow) and SQL.
- Hands-on experience with MLOps tools like MLflow, Kubeflow, Docker, Kubernetes, and CI/CD pipelines.
- Deep understanding of Generative AI and NLP technologies, including transformer models (e.g., GPT, BERT), Hugging Face, and LangChain.
- Strong background in data engineering using PySpark and distributed data processing.
- Experience with one or more major cloud platforms (AWS, Azure, or GCP) is a plus.
Mandatory Skills:
- Expertise in data architecture, data modeling (dimensional, relational), and hybrid cloud/on-prem environments.
- Strong proficiency in Python and PySpark; advanced SQL skills.
- Familiarity with entity resolution and data governance tools.
- Exposure to Generative AI, Machine Learning, or LLM integration.
- Preferred experience with financial products, general ledger (GL) data, or finance operations.
Seniority level
- Seniority levelMid-Senior level
Employment type
Job function
- Job functionOther
- IndustriesIT Services and IT Consulting
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