AI/ML Engineer

#WeAreHiring

Role: AI/ML Engineer (Voice AI • Conversational Systems • Execution & Optimisation)

Location: Chennai (Hybrid)

Company: Allsec AI

Website: www.allsec.ai

Engagement Type: Full-Time | Early-Stage Start-up

About Allsec AI Allsec AI is an

AI-native, digital-first, omni-channel CX managed services company . We help enterprises unlock scalable, efficient, and real-time customer experience operations through agentic AI, intelligent workflows, and automation. We are

redefining outsourcing by delivering packaged, outcome-led services, with

full ownership of delivery outcomes , not just technology.

About This Role The core engineering challenge of this role is to

create AI voice-based CX within a trifecta of cost, quality, and latency . You will execute on decisions that balance all three (across voice models, language models, and real-time pipelines) in a production CX environment where those trade-offs have direct business consequences. You will be working on a

live, multi-model AI platform where your responsibility lies in contributing meaningfully to making it better, faster, and more economical , under the technical direction of the Principal Engineer. You will be expected to ship, optimise, debug, and iterate, often across layers of the stack you may not have originally written. Beyond the core platform, you will support the Principal Engineer and operational leadership team in developing new use cases, building out conversation flows, and contributing to the product’s expansion into new verticals. If you are someone who thinks about AI not just as a technical system but as something that has to work for a real person on the other end of a phone call, this role is built for you.

Key Responsibilities Voice AI & Model Optimisation

• Work on and improve the end-to-end voice pipeline — STT, TTS, interruption handling, silence detection, and turn-taking logic.
• Execute on optimisations across the cost-quality-latency trifecta: implement caching, streaming, and model selection decisions under technical guidance.
• Improve STT accuracy across accents, call environments, and language variations through systematic testing and tuning.
• Evaluate new voice and language models and provide recommendations to the Principal Engineer on provider selection.

LLM Integration & Pipeline Engineering

• Build, maintain, and improve LLM pipelines including RAG, prompt engineering, and context management.
• Work across multiple LLM providers — benchmarking based on cost, latency, and output quality as directed.
• Contribute to evaluation frameworks that measure model performance across cost, quality, and latency dimensions.
• Manage and improve vector databases, embedding pipelines, and retrieval systems that power the knowledge layer.

Use Case & Conversation Support

• Support the translation of client requirements into conversation flows and call scripts, working alongside the operational and business team.
• Build and refine branching logic for multi-turn voice conversations — handling edge cases, fallbacks, and escalation paths.
• Contribute ideas and execution on new use cases as the business grows.
• Assist the Principal Engineer in extending existing flows into new industry verticals — from BFSI to healthcare, retail, and beyond.

Platform & Infrastructure

• Be operationally self-sufficient — deploy, monitor, and debug your own work across the existing multi-cloud stack (Digital Ocean, Azure, GCP).
• Contribute to basic UI/UX improvements where the AI layer meets the client-facing product.
• Participate in code reviews, maintain clean and documented code, and work within engineering standards set by the Principal Engineer.

Requirements

• 4+ years of software development experience — strong development fundamentals are non-negotiable; you must be able to write clean, production-quality code.
• Hands-on experience with LLM APIs and integration (OpenAI, Anthropic, Gemini, or equivalent) in a live product environment — not just experimentation.
• Familiarity with RAG pipelines, vector databases (Pinecone, FAISS, or similar), and prompt engineering.
• Enough cloud and DevOps competence to own your own deployments — you do not need a separate DevOps resource to ship your work.
• A builder mindset — comfortable inheriting a codebase you did not write, figuring things out without a playbook, and delivering reliably under technical direction.
• Interest in the product and use case side of AI — not just the engineering.

Nice to Have

• Experience working on conversational AI, voice bots, or contact centre technology.
• Working experience with STT and/or TTS systems.
• Exposure to MLOps practices — model versioning, evaluation pipelines, and testing of model variants.
• Prior start-up or early-stage product experience where you have worked across multiple areas of the stack.
• Comfort working in a multi-stakeholder environment with both technical and non-technical collaborators.

Why Join Allsec AI

• Work on one of the hardest and

most interesting problems in applied AI : making voice AI that actually works, for real enterprise clients.

• Learn directly from a Principal Engineer with deep technical expertise, in an environment where good work is visible and recognised.
• The work you do will have

direct, visible impact on a product used by real enterprise clients.

• A front-row seat to

building an AI-native company from its earliest stage , with equity upside for the right candidate.

• Competitive compensation commensurate with experience.

How to Apply Interested candidates, please send your resume to:

careers@allsec.ai, gukhan.allsec@outlook.com