Technogise provides software engineering services. We build software for clients who buy the quality of the work and business outcomes rather than the headcount. Over the last year, we built an AI-augmented way of delivering it and won business on that basis. This role grows that business and keeps the capability behind it ahead of what we offer.
Why this role exists
You’ll use your technology and engineering expertise to grow the business and strengthen the organisation’s capabilities, with AI at the core.
This includes marketing, outreach, account growth, and hiring.
You’ll execute, support teams, and improve processes wherever your expertise can make the biggest impact.
What you will own: growing the business
Pre-sales and solutioning. Work with the sales side on accounts in the presentation and proposal stage: understand the customer's problem, shape the technical solution, size it, and present it. You will be in front of customers, and you need to hold a technical conversation with them without a manager translating.
Accounts and the commercial side. You need to understand how a proposal becomes a staffed team, what bench and utilisation cost, why a delivery slip is a commercial problem before it is a technical one, and how an account grows from one team to three. You will be asked for a view on pricing and staffing shape, not only on architecture.
Team Readiness & Capability Building. Drive cross-technology training and forward-looking learning initiatives to prepare teams for emerging technologies, AI adoption, and evolving client needs.
Thought Leadership & Sales. Drive technical thought leadership through case studies, deep dives, and blogs that showcase our expertise and build credibility. Translate this content into marketing, lead generation, and sales opportunities by helping prospects understand how we solve their challenges. Use AI to scale content creation while ensuring technical depth, quality, and business relevance.
Hiring. Take the interviews where the technical call is hard, keep the bar aligned with the work we are selling now, and give the recruitment team a way to read a candidate's AI claims that is better than counting tool names on a resume.
What you will own: Raising what we can do
Capability and enablement. Keep our AI-augmented engineering practice up to date and drive its adoption through maturity assessments, training, hands-on team support, and evaluation of new tools. Help engineers, recruitment, and people teams understand and build the AI capabilities we need..
Capability we can sell. Turn things we can do into things we can offer. A new service line starts as an internal spike, becomes a demo, then a proposal. Recent examples are agentic AI engagements, infrastructure automation, and data engineering.
Technical cover on delivery. Support multiple active engagements by helping delivery leads resolve tough design decisions, validate estimates, review code and architecture, and address quality issues early. Stay independent of individual teams to provide an objective, cross-project perspective. Champion AI while ensuring the focus remains on delivering real outcomes.
What we are looking for
- Genuinely strong hands-on engineering. You still read and write code, and you can hold your own in a design review with the sharp engineers in the room. This one is not negotiable.
- An instinct for the business of a services firm: proposals, staffing, utilisation, and how repeat business actually happens.
- Hands-on with LLM-based development: coding agents, agentic workflows, retrieval over private data, and judging what a model actually produced
- Comfortable in front of a customer, including one who is technical and sceptical
- A teaching instinct. You like making other engineers better and you have evidence of having done it.
- Judgement about quality. You can tell the difference between a demo that works and a system that will hold
- Able to work through functions you do not run, since marketing, recruitment and sales own their own work and you make it sharper
- Comfortable being measured by business you helped win and capability you raised, rather than by software you personally shipped
Useful but not required: exposure to data engineering, a public body of writing or open-source work, experience running training for engineers.
Skills:- Artificial Intelligence (AI), Java, Go Programming (Golang) and Python
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