Work Mode: Work from Office

Shift Timings: 10 AM to 7 PM IST

Job Responsibilities

- Team Leadership and Autonomy: Lead and mentor a team of engineers, driving architectural choices, establishing engineering best practices, and executing milestones independently under minimal supervision.
- Complex Data Extraction and Parsing: Design and optimize data ingestion routines for highly complex data, managing seamless extraction, transformation, and normalization from PDFs (including OCR/unstructured text), CSV, JSON, and complex databases.
- Intelligent Data Pipelines: Architect, build, and maintain robust end-to-end data pipelines and ETL/ELT workflows tailored for AI systems, ensuring structural integrity, strong data schema validation, and optimized throughput.
- Agentic Architecture Design: Architect and scale distributed, multi-agent systems using the Agent-to-Agent (A2A) protocol to manage cross-platform orchestration, decentralized task routing, and autonomous agent collaboration.
- Cognitive Modeling Sequential Learning: Build, optimize, and maintain custom sequence-to-sequence networks, time-series, and text prediction models utilizing LSTM (Long Short-Term Memory) networks and specialized deep learning architectures.
- Knowledge Infrastructure Ontologies: Design and implement robust enterprise knowledge bases, developing complex ontologies, semantic data models, and knowledge graphs to enforce deterministic reasoning and domain-specific rules within AI agents.
- Hybrid Retrieval (RAG) Pipelines: Develop and maintain hybrid Retrieval-Augmented Generation (RAG) systems that balance on-premise data security constraints with scalable, advanced cloud-based RAG models and vector databases.
- On-Premise Cloud Deployment: Lead the deployment of foundational models and agent runtimes on on-premise infrastructure (handling bare-metal or private cloud security constraints) as well as continuous delivery to major cloud platforms (AWS, GCP,



or Azure).
- Core Software Engineering: Write production-grade, highly optimized, and clean code exclusively in Python, implementing asynchronous patterns and robust API integrations.

Requirements Qualifications

- Experience Leadership: 7+ years of hands-on experience in software or data engineering, with a proven track record of leading technical teams and driving projects forward autonomously.
- Data Mastery: Exceptional understanding of data structures and a robust background in handling complex data variety, including structured and unstructured data parsing (PDFs, CSV, JSON).
- Data Pipeline Expertise: Deep, hands-on experience designing scalable data pipelines (using orchestration tools like Apache Airflow, Prefect, or Mage) to feed large-scale AI applications.
- Core Language: Expert-level mastery of Python and its scientific/AI ecosystem (PyTorch, TensorFlow, NumPy, Pandas).
- Deep Learning and NLP: Solid theoretical and practical understanding of recurrent neural networks, specifically LSTMs, alongside transformers, text embeddings, and tokenization strategies.
- Data and Knowledge Engineering: Proven track record in graph databases (e.g., Neo4j), RDF frameworks, ontology editing tools, semantic indexing, and high-concurrency vector databases (e.g., Pinecone, Milvus, Qdrant, Chroma).
- Infrastructure Capabilities: Strong background in orchestration and virtualization tooling (Docker, Kubernetes), private server configuration, and deploying models within strict containerized, on-premise hardware environments.
- Cloud AI Ecosystems: Hands-on experience with managed cloud-native AI platforms and APIs (e.g., OpenAI API, Anthropic Claude, AWS Bedrock, Google Vertex AI).

Disclaimer: This job posting and Location has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 AI & Data Automation Engineer (Hyderabad)
🏢 mroads
📍 Hyderabad