Data Scientist / ML Engineer at Franklin Templeton designing and productionizing machine learning systems for business solutions. Collaborating with teams to deliver scalable and reliable ML solutions.
About the role
- Senior Data Scientist developing advanced ML and Generative AI applications at Emerson. Leading projects in AI solutioning, deployment, and cloud-based environments.
Responsibilities
- Design, develop, and deploy advanced ML, DL and Generative AI models (LLMs, Transformers, Diffusion, GANs, VAEs) for NLP, multimodal, forecasting, recommendation, and intelligent automation use cases.
- Lead data-driven GenAI solutioning, including problem framing, feature engineering, dataset curation and statistical validation for enterprise AI applications.
- Perform exploratory data analysis (EDA), bias detection, data quality checks, and advanced feature engineering on structured and unstructured data (text, image, tabular).
- Monitor and manage data drift, model drift, hallucination risks and performance degradation, and define retraining and recalibration strategies.
- Engineer end-to-end RAG pipelines and multi-agent workflows using LangChain, Copilot, MCP.
- Build and optimize RAG pipelines, semantic search, and contextual reasoning workflows using embeddings, chunking strategies, hybrid retrieval, and reranking techniques.
- Integrate vector DBs (PostreSQL, Pinecone, Weaviate, Chroma, FAISS, Redis) and graph DBs (Neo4j, Postgres/pgvector) for semantic retrieval and contextual reasoning.
- Optimize foundation models (GPT, LLaMA, Mistral, Falcon) via prompt engineering, RLHF, LoRA, quantization, and hyperparameter tuning.
- Build scalable AI solutions using Azure AI/ML (preferred) with containerized deployments (Docker, Kubernetes).
- Apply MLOps, LLMOps best practices: CI/CD, model versioning, drift detection, observability, and lifecycle management with MLflow, Kubeflow, Airflow, and monitoring tools.
- Develop secure AI pipelines and APIs with Python, FastAPI/Flask, RBAC, OAuth2, JWT, and encryption standards.
- Conduct model optimization: prompt engineering, hyperparameter tuning, cross-validation, and performance monitoring.
- Use tools like Azure Machine Learning, OpenAI API, HuggingFace, or custom PyTorch/TensorFlow-based models.
- Implement AI safety, bias mitigation, interpretability (SHAP, LIME, and compliance guardrails (GDPR, HIPAA, ISO).
- Collaborate with cross-functional teams to deliver enterprise-grade copilots, assistants, and reusable AI components.
- Document AI design, model workflows, and deployment pipelines for audit readiness and knowledge sharing.
Requirements
- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, or a related field over 7+ years.
- Proven experience as a Data Scientist Developer or in a similar role and proficiency in Python
- Experience with AI on Azure (must), including Azure OpenAI, Azure ML, and related services.
- Deep hands-on experience in Python, CUDA, SQL, proficient with TensorFlow, PyTorch, Keras.
- Familiarity with security, bias mitigation, and responsible AI frameworks.
- Experience with MLOps practices and tools for deploying, tracking, and updating models.
- Excellent problem-solving, communication, and team collaboration skills.
- Preferred Qualifications: Certifications in AI/ML from Microsoft, AWS or Coursera/edX, Exposure to enterprise use cases in industries such as manufacturing, finance and other, Experience with AutoML, LLMOps, and performance benchmarking tools, Understanding of semantic search, knowledge graphs, and contextual recommendation engines, Hands on MLOps experience, with an appreciation of the end-to-end CI/CD process, Certified in Azure AI Fundamentals (AI-900), Azure AI Engineer Associate (AI-102), Azure Developer Associate, Experience with big data technologies
Benefits
- comprehensive medical and insurance coverage
- Work Authorization Sponsorship
- hybrid work set up
- Safe Working Environment
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