Working Hours :
Full Time
Locations :
Hyderabad
Experience :
6 10 years
apply now
apply now
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 (010)
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.
