We are seeking a highly skilled Senior Machine Learning Engineer with deep expertise in NLP, large language models, and model deployment. This role focuses on building and operationalizing the core ML components that power our next generation search, summarization, and answer generation capabilities. You will work closely with Applied Scientists, Product, and Engineering to bring advanced models from prototype to reliable, scalable production systems.
This role requires close collaboration with teams across Canada, the UK, and India. To accommodate the time zone differences, you will be expected to work from [11 am - 7:30 pm IST].
💻 What You’ll Be Doing
Advanced Modeling & Applied Science
Build and productionize LLM and NLP models across retrieval, summarization, classification, and generative tasks by developing optimized embedding pipelines, prompt strategies, and fine tuning methods while translating experimental prototypes into robust components that consistently perform under production conditions.
Improve model accuracy, relevance, and robustness through structured evaluation frameworks, systematic error analysis, and iterative experimentation that ensures predictable behavior over time.
System Architecture & Engineering
Design and implement scalable ML services and inference pipelines in Python using modern ML frameworks, incorporating efficient serving strategies such as batching, caching, streaming, and performance tuned deployment patterns that meet latency, reliability, and throughput requirements.
Integrate models with retrieval systems, feature stores, and knowledge sources while applying strong engineering discipline in automated testing, observability, logging, and operational readiness to support durable, maintainable ML systems.
Contribute to core architectural decisions that balance modeling complexity with performance, maintainability, and long term extensibility.
Delivery, Collaboration & Leadership
Translate complex NLP and LLM product requirements into structured engineering plans with clear milestones while collaborating closely with Product, Engineering, and Applied Science partners to align expectations, remove constraints, and deliver measurable customer impact.
Participate in technical design reviews and champion improvements in ML engineering practices including deployment standards, model QA, code quality, and operational excellence while providing informal mentorship on modern NLP techniques and scalable model serving.
Demonstrate strong ownership and attention to detail by driving high quality delivery in a fast moving environment focused on reliability, clarity, and meaningful outcomes.
✨ What You’ll Bring to the Team
Proven Expertise in Machine Learning, Language Models, and NLP
4+ years applying ML in production with practical depth in NLP, LLM workflows, embeddings, or retrieval augmented systems.
Hands on and deep experience with transformer models, embedding based methods, and retrieval augmented techniques, prompt structured or fine tuned LLMs, for reasoning and generation tasks.
Ability to turn prototypes into stable engineering solutions that perform consistently in production environments.
System Design, Scalability, and Software Engineering Rigor
Strong proficiency in Python, modern ML frameworks such as PyTorch or TensorFlow, and API or microservice development.
Experience building scalable and reliable ML services with attention to latency, observability, testing, deployment patterns, and runtime durability.
Ability to design robust agentic components including control flow, state management, and integrations with retrieval and knowledge systems.
Business and Product Acumen
Ability to frame technical decisions in terms of customer impact, product value, and measurable improvements to workflow efficiency or answer quality.
Strong collaboration skills with cross functional partners to align priorities and drive clear, grounded execution.
Comfort operating with ownership and urgency in a fast moving environment focused on delivering meaningful outcomes.
Where You’ll Work 📍
This is a remote-first opportunity with the advantages of working flexibly across India.
We are HQ’d in Canada, with established hub regions around the world where we hire from. Our employees (or Loopers, as we call ourselves!) live and work in 🇨🇦 Canada (British Columbia and Ontario), 🇮🇳 India (HQ in Ahmedabad), and 🇬🇧 England (London).
Our India/UK hub employees work according to the business hours stated in the job description above. This is intentionally designed to enable our global teams to have overlapping hours for collaboration.
You’ll collaborate with your teams virtually across the UK, India, and North America (we’re just a Zoom call and Slack message away!) with core sync hours and focus time for headsdown work 🙇🏾 during the workday
We encourage asynchronous collaboration to effectively work as a global #OneTeam!
Why You’ll ❤️ Working at Loopio
Your manager supports your development by providing ongoing feedback and regular 1-on-1s, we leverage Lattice for our 1:1s and performance conversations
You will have the opportunity to elevate 🪄 your craft and the opportunity to explore your creativity, with a dedicated professional mastery allowance for more learning support! We encourage experimentation and innovative thinking to drive business impact.
We’ll set you up to work remotely with a MacBook laptop 🍏, a monthly phone and internet subsidy, and a work-from-home budget to help get your home office all set up.
You’ll be joining a supportive culture that has thoughtfully built out opportunities for connections in a remote first environment.
Participate in 🎤 townhalls, AMA (Ask-Me-Anything), and quarterly celebrations to celebrate the big wins and milestones as #oneteam!
Our four active Employee Resource Groups offer opportunities for employees to learn and connect year-round.
You’ll be a part of an award-winning workplace 🏆with an opportunity to make a big impact on the business.
Questioning your qualifications? Read this ‼️ Hi there, we recognize that all too often, potential candidates don’t apply for a position simply because they don’t hit every single criteria included in the job description—particularly members of underrepresented groups.
Whether or not your experience ✅
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