Data Scientist Agentic AI

Python, PyTorch, TensorFlow, LLMs, LangChain, Multi-Agent Systems, RAG, Knowledge Graphs, Vector Databases (FAISS, Pinecone), Kubernetes, Cloud Deployment

Hyderabad (Onsite)

Job Title : Data Scientist Agentic AI

Location : Hyderabad, India (Onsite) Company : Spectraforce

Role Overview :

We are seeking a Data Scientist with deep technical expertise in large language models, autonomous agent design, and intelligent system orchestration. This role focuses on building self-directed, context-aware AI systems that can reason, learn, and interact with humans and software environments.

Key Responsibilities :

- Design and build Agentic AI architectures capable of autonomous task execution, reasoning, and planning.

- Develop and optimize LLM-driven multi-agent systems that coordinate, collaborate, and adapt dynamically.

- Implement retrieval-augmented generation (RAG) and memory-based reasoning pipelines for intelligent context use.

- Conduct prompt engineering, few-shot learning, and reinforcement learning to fine-tune agent behavior.

- Build and manage knowledge graphs, embedding databases, and semantic retrieval layers to power reasoning.

- Architect data ingestion, context extraction, and interaction layers that enable AI agents to interface with real-world APIs, databases, and systems.

- Lead experimentation, evaluation, and optimization cycles to improve agent accuracy, autonomy, and decision quality.

- Collaborate with AI engineers and product teams to integrate agents into scalable production systems.

Technical Skills :

Core AI and ML Expertise :

- Proficiency in Python, PyTorch, TensorFlow, or JAX.

- Strong understanding of transformer-based architectures, LLM tuning, and prompt optimization.

- Hands-on experience with Agentic frameworks such as LangChain, LlamaIndex, CrewAI, or similar orchestration tools.

- Deep knowledge of multi-agent systems, goal decomposition, and reasoning loops.

Data and Infrastructure Engineering :

- Experience with vector databases such as FAISS, Pinecone, or Milvus, and graph systems such as Neo4j or ArangoDB.

- Proficient in data pipeline design, feature computation, and knowledge representation.

- Familiarity with Docker, Kubernetes, and cloud-based AI deployment environments such as AWS, GCP, or Azure.

Advanced Capabilities (Preferred) :

- Exposure to self-improving agent architectures, meta-learning, or tool-using AI systems.

- Understanding of AI alignment, explainability, and autonomous evaluation mechanisms.

- Practical experience integrating real-time APIs, workflow orchestration, or decision intelligence systems.

What You Will Work On :

- Designing autonomous reasoning systems that adapt to dynamic objectives.

- Building scalable AI agent platforms that integrate perception, reasoning, and execution layers.

- Developing intelligent orchestration frameworks that enable efficient human-AI collaboration.