Lead Applied AI Research Engineer

Location: Hyderabad, India

Employment Type: Full-time

Work Model: On-site

Business Unit: Winfo Research Labs

Reporting To: Head of Winfo Research Labs

Role Type: Hands-on technical leadership and individual contributor role

Role Description

The Lead Applied AI Research Engineer will be WRL's hands-on technical research leader responsible for converting complex and often ambiguous business problems into structured AI research and experimentation programs.

The role begins with understanding the business problem, breaking it into smaller decision and capability components, and determining which parts can be solved using established methods, which require experimentation, and which present genuine research opportunities. The Lead Applied AI Research Engineer will then formulate hypotheses, compare alternative approaches, build and evaluate proof-of-concepts, and determine whether a proposed solution has sufficient technical and business evidence to move into development.

This is not a purely academic research role, a conventional data science role, or a production engineering role. It is an applied research and technical leadership position for someone who can move comfortably between business problem discovery, AI research, experimentation, prototyping, evaluation, and solution architecture.

The successful candidate will work closely with business stakeholders, domain specialists, AI engineers, data engineers, cloud architects, product teams, and deployment specialists. Once an approach has been validated, the researcher will produce a clear engineering handoff covering the solution architecture, experimental evidence, evaluation results, data requirements, risks, constraints, and recommended implementation path.

Required Qualifications

Demonstrated experience translating business or customer problems into applied AI solutions.
Strong ability to break complex problems into researchable and executable components.
Hands-on experience designing experiments and creating proof-of-concepts.
Strong understanding of machine learning, deep learning, generative AI, LLMs, RAG, and agentic AI.
Proficiency in Python and practical experience with modern ML and AI development frameworks.
Experience defining evaluation criteria and comparing alternative models, architectures, or workflows.
Ability to communicate technical findings, trade-offs, risks, and recommendations to both technical and non-technical audiences.
Experience collaborating with software engineering, data, cloud, architecture, or product teams.
Demonstrable portfolio of AI solutions, PoCs, prototypes, research projects, or shipped capabilities.
Ability to work effectively in an ambiguous, rapidly evolving research and innovation environment.

Preferred Qualifications

Master's degree or PhD in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related technical field.
Experience with enterprise AI use cases and enterprise application environments.
Understanding of cloud AI platforms, model serving, data pipelines, APIs, security, and observability.
Experience developing reusable AI components, accelerators, or reference architectures.
Experience with optimization, multimodal AI, knowledge graphs, fine-tuning, synthetic data, or advanced evaluation methods.
Experience contributing to patents, publications, open-source projects, technical articles, or research communities.
Previous experience in an applied research group, innovation lab, product organization, startup, or customer-facing technical role.
Experience mentoring researchers, data scientists, or AI engineers.

A postgraduate research qualification is valuable but should not replace evidence that the candidate has previously converted a customer or business problem into a credible, evaluated solution. The WRL hiring material similarly lists a PhD as a bonus while emphasizing demonstrated AI solutions, research depth, technical literacy, and buildable specifications.

What Success Looks Like

A successful Lead Applied AI Research Engineer will:

Turn ambiguous business opportunities into well-defined research and experimentation plans.
Build PoCs that answer explicit feasibility and value questions.
Use objective evaluation evidence to recommend whether an idea should proceed.
Provide Development with clear, reproducible, and implementation-ready handoffs.
Establish a repeatable applied research and PoC methodology for WRL.
Create reusable knowledge, benchmarks, reference patterns, and technical assets.
Help WRL avoid both premature production investment and endless experimentation.
Build technical credibility with customers, internal stakeholders, researchers, and engineers.
Develop junior team members while remaining directly involved in high-value technical work.

Lead Applied AI Research Engineer - Principal Consultant