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Senior AI Scientist (ML + Data Engineering Focus)

Working Hours : Full Time

Locations : Hyderabad

Experience : 6 –10 years


About the Role:

Soothsayer Analytics is a global AI and Data Science consultancy headquartered in Detroit, with a thriving delivery center in Hyderabad. We design and deploy end-to-end custom Machine Learning solutions spanning predictive analytics, optimization, NLP, and GenAI that help leading enterprises forecast, automate, and gain a competitive edge.

Join us to tackle high-impact, cross-industry projects where your ideas move rapidly from concept to production, shaping the future of data-driven decision-making.

We seek a Senior AI Scientist with strong ML fundamentals and data engineering expertise to lead the development of scalable AI/LLM solutions. You will design, fine-tune, and deploy models (e.g., LLMs, RAG architectures) while ensuring robust data pipelines and MLOps practices.


Key Responsibilities

  • AI/LLM Development:
  • Fine-tune and optimize LLMs (e.g., GPT, Llama) and traditional ML models for production.
  • Implement retrieval-augmented generation (RAG), vector databases, and orchestration tools (e.g., LangChain).
  • Data Engineering:
  • Build scalable data pipelines for unstructured/text data (e.g., Spark, Kafka, Airflow).
  • Optimize storage/retrieval for embeddings (e.g., pgvector, Pinecone).
  • MLOps & Deployment:
  • Containerize models (Docker) and deploy on cloud (AWS/Azure/GCP) using Kubernetes.
  • Design CI/CD pipelines for LLM workflows (experiment tracking, monitoring).
  • Collaboration:
  • Work with DevOps to optimize latency/cost trade-offs for LLM APIs.
  • Mentor junior team members on ML engineering best practices.

Required Skills & Qualifications

  • Education: MS/PhD in CS/AI/Data Science (or equivalent experience).
  • Experience: 6+ years in ML + data engineering, with 2+ years in LLM/GenAI projects.

Skills Matrix

Candidates must submit a detailed resume and fill out the following matrix:

Skill


Details


Skills Last Used


Experience (months)


Self-Rating (0–10)


Python



ML



SQL/NoSQL



Apache Spark/Kafka



LLM Frameworks (LangChain, etc.)



MLOps (Docker/K8s)



Cloud (AWS/Azure/GCP)



Vector Databases (Pinecone, pgvector)



Instructions for Candidates:

  • Provide a detailed resume highlighting projects related to LLMs, data engineering, and MLOps.
  • Fill out the matrix above with accurate dates, experience duration, and self-ratings.

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