AHEAD builds platforms for digital business. By weaving together advances in cloud infrastructure, automation and analytics, and software delivery, we help enterprises deliver on the promise of digital transformation.
At AHEAD, we prioritize creating a culture of belonging, where all perspectives and voices are represented, valued, respected, and heard. We create spaces to empower everyone to speak up, make change, and drive the culture at AHEAD.
We are an equal opportunity employer, and do not discriminate based on an individual's race, national origin, color, gender, gender identity, gender expression, sexual orientation, religion, age, disability, marital status, or any other protected characteristic under applicable law, whether actual or perceived.
We embrace all candidates that will contribute to the diversification and enrichment of ideas and perspectives at AHEAD.
As a Senior Technical Consultant, AI Services, you are responsible for the hands-on delivery of AI solutions within AHEAD client engagements. You are an engineer and client-advisor - you turn solution designs into working, production-grade GenAI / Agentic AI workflows, and you take them all the way into production - deployed, running, and reliable in live client environments.
You will collaborate with clients and interdisciplinary teams, which includes engineers, product owners, domain experts to understand real business needs and solve complex challenges across industries. You will work alongside senior consultants and architects, contribute to the technical direction and own delivery.
Solution Delivery & Production Deployment
Own the hands-on delivery of AI solutions end-to-end: build, test, integrate, deploy, and ship GenAI services and agentic workflows into production.
Take solutions from prototype to production handling deployment, release, versioning, and rollback, and keep them running reliably once they are live.
Make sound design and trade-off decisions as you build, and bring the hard, cross-cutting calls into the team’s technical discussions contributing to the architecture, not just consuming it.
Produce and maintain your own estimates, task breakdowns, and delivery status; surface risks, blockers, and dependencies early.
GenAI Engineering & Implementation
Build agentic and multi-step workflows using orchestration frameworks and platform patterns (e.g., LangGraph, AgentCore, LangChain).
Develop robust tooling and APIs for agents, with clear input/output schemas, error contracts, versioning, and observability hooks.
Consume retrieval/RAG and search abstractions to improve grounding and reliability, tuning parameters (top-k, scoring, filters).
Quality, Observability & Governance
Own the operational health of the workflows you build: monitoring, alerting, troubleshooting, and iterative improvement.
Set up the observability and evaluation tooling for the solutions you build including tracing, logging, and metrics through an LLM observability stack (e.g., Langfuse, LangSmith), and quality, regression, and safety checks through evaluation frameworks (e.g., DeepEval, Ragas).
Operate within established platform, security, and governance guardrails (RBAC, data access boundaries, PII handling, logging, audit) instead of building one-off mechanisms.
Collaboration & Enablement
Partner with product managers, business stakeholders, and UX to turn problem statements and evaluation criteria into concrete, production-ready workflows.
Participate actively in design reviews, code reviews, and architecture discussions, keeping solutions maintainable, observable, and aligned to platform standards.
Contribute to internal enablement (playbooks, examples, reusable patterns) and act as a high adopter of AI tools (e.g., Glean, Devin, Windsurf, Claude) to accelerate design, development, testing, and documentation.
- Bachelor’s or master’s degree in computer science, Statistics, Mathematics, or a related quantitative field.
- Minimum of 5 years of experience in a data science-related role, with a focus on machine learning and deep learning.
- Strong Python coding skills with an emphasis on writing efficient, scalable, and maintainable code.
- Experience with developing and training custom deep learning models using TensorFlow, PyTorch, or scikit-learn.
- Hands-on experience with Jupyter Notebooks, Azure Machine Learning Studio, Azure OpenAI, AWS SageMaker, nVidia AI Enterprise & DGX platforms
- Hands-on experience with leading open source and major model providers such as LLama, Anthropic's Claude, Open AI. Etc.
- Solid understanding of transformer architectures, attention mechanisms, and other advanced deep learning concepts.
- Knowledge of generative AI concepts, including fine-tuning, transfer learning, and RAG methods.
- Experience with the machine learning lifecycle, including model deployment, monitoring, drift detection/retraining, and canary testing.
- Strong communication and collaboration skills, with the ability to present complex technical information to both technical and non-technical audiences.
- Experience in pre-sales activities, including project scoping, estimation, and solution design.
Demonstrated experience deploying and operating GenAI or agentic AI solutions in production taking them beyond prototypes and demos into live, reliable systems, including release, versioning, monitoring, and ongoing operation.
Practical, hands-on experience integrating LLM/GenAI capabilities (e.g., AWS Bedrock, Azure AI, OpenAI, Anthropic), including prompt and system design for reliability and control, and handling structured outputs (JSON schemas, tool/function calling).
Experience in a cloud-native AWS/Azure environment, including serverless patterns (Lambda or similar), environment configuration and secrets management, and logging, metrics, and basic observability/debugging.
Strong software engineering fundamentals: Git, testing, code review, CI/CD-friendly patterns, and clean code practices.
Effective collaboration and communication skills, with the ability to work closely with product, and domain experts to converge on pragmatic, production-ready solutions.
Awareness of security, governance, and responsible AI in an enterprise context: RBAC and data access boundaries, PII and sensitive-data handling, and working within established platform guardrails and governance processes.
Familiarity with MLOps, data platforms, or observability tools used to track quality, performance, and usage of GenAI features.
Evidence of being an early, high adopter of AI tools in your own workflow (code assistants, AI debuggers, documentation generators, experimentation tools).
Why AHEAD:
Through our daily work and internal groups like Moving Women AHEAD and RISE AHEAD, we value and benefit from diversity of people, ideas, experience, and everything in between.
We fuel growth by stacking our office with top-notch technologies in a multi-million-dollar lab, by encouraging cross department training and development, sponsoring certifications and credentials for continued learning.
India Employment Benefits include:
Comprehensive health insurance coverage for employees, with options to extend coverage to dependents
Paid time off and company holidays, along with additional leave benefits as per policy
Flexible work arrangements, supporting work-life balance
Learning and development opportunities to support continuous growth and upskilling
Employee wellness initiatives and programs focused on physical and mental well-being
Retirement and statutory benefits in line with India regulations
Inclusive and people-first culture, with a strong focus on collaboration and ownership
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
