Hands-on applied AI research leader in Hyderabad, driving experiments and PoCs. Requires deep technical expertise in machine learning, genAI, LLMs, and Python, with a focus on converting business problems into validated solutions.

About the Company

Winfo Solutions is a global technology company with its research and development arm, Winfo Research Labs (WRL), operating from Hyderabad, India. The company specializes in building enterprise-grade AI solutions, focusing on the intersection of applied research and practical engineering. Winfo Solutions serves a diverse clientele across industries, leveraging advanced analytics, machine learning, and AI to solve complex business challenges. The firm is known for its innovation-first culture, encouraging experimentation and the conversion of cutting-edge research into deployable products.

Role Overview

The Lead Applied AI Research Engineer is a hands-on technical leadership role within Winfo Research Labs (WRL). This is not a purely academic research position, a conventional data science role, or a production engineering role. It is an applied research and technical leadership position that bridges the gap between business problem discovery, AI research, experimentation, prototyping, evaluation, and solution architecture. The successful candidate will work at the forefront of AI innovation, translating ambiguous business problems into structured research programs and building proof-of-concepts (PoCs) that answer critical feasibility and value questions. This role involves direct collaboration with business stakeholders, domain specialists, AI engineers, data engineers, cloud architects, product teams, and deployment specialists. The person in this role must be comfortable with ambiguity, thrive in a rapidly evolving innovation environment, and be capable of driving an idea from a sketch on a whiteboard to an implementation-ready engineering handoff.

Key Responsibilities

• Business Problem Translation: Engage with business stakeholders and domain experts to understand complex, often ambiguous problems, and decompose them into smaller decision and capability components. Assess which components can be solved with established methods, which require experimentation, and which represent genuine research opportunities.
• Research and Experimentation: Formulate hypotheses, design experiments, and compare alternative approaches (models, architectures, workflows) using objective evaluation criteria. Build and evaluate proof-of-concepts (PoCs) that provide solid technical and business evidence for the viability of proposed solutions.
• Technical Leadership: Act as the technical research leader within WRL, setting the direction for applied research projects, and ensuring that projects maintain a balance between innovation and practicality. Provide hands-on guidance and mentorship to junior researchers and data scientists.
• Engineering Handoff: Once a solution is validated, produce clear engineering handoffs covering solution architecture, experimental evidence, evaluation results, data requirements, risks, constraints, and a recommended implementation path. Ensure the handoff is reproducible and ready for development.
• Methodology and Reusability: Establish a repeatable applied research and PoC methodology for WRL. Create reusable knowledge, benchmarks, reference patterns, and technical assets that can accelerate future projects.
• Stakeholder Communication: Clearly communicate technical findings, trade-offs, risks, and recommendations to both technical and non-technical audiences. Build technical credibility with customers, internal stakeholders, researchers, and engineers.

Tech Stack

The role requires hands-on proficiency with the following technologies and tools:

• Programming Language: Python
• Machine Learning / Deep Learning Frameworks: TensorFlow, PyTorch, Keras, scikit-learn
• Generative AI & LLM Libraries: LangChain, LlamaIndex, Hugging Face Transformers, OpenAI/Anthropic LLMs
• RAG and Agentic AI: Vector databases (e.g., Pinecone, Weaviate), agent orchestration (e.g., AutoGPT, LangGraph), Retrieval-Augmented Generation
• Cloud Platforms: AWS, Azure, Google Cloud, with services like SageMaker, Bedrock, Azure ML
• Model Serving & APIs: FastAPI, Docker, Kubernetes, REST APIs, MLflow, Kserve
• Data Engineering: SQL, NoSQL, Apache Spark, ETL pipelines, data processing
• Evaluation & Observability: Weights & Biases, MLflow, Evidently AI, logging tools like Prometheus/Grafana
• Version Control: Git, GitLab/GitHub

experience translating business problems into applied AI solutions, experience breaking problems into researchable components, hands-on PoC development, ML/DL/genAI/LLM/RAG/agentic understanding, Python proficiency, evaluation criteria definition, communication skills, collaboration with engineering teams, portfolio of AI solutions, comfort with ambiguity, Master's/PhD preferred but not mandatory.

Work on high-impact AI research problems with direct business significance. Opportunity to define a new research practice from the ground up. Work alongside domain specialists, data engineers, and cloud architects. Professional development through mentoring junior researchers. Exposure to enterprise AI use cases and cutting-edge technologies including LLMs and agentic AI.