Principal AI/ML Engineer

We are on the lookout for a Principal AI/ML Engineer! As a Principal AI/ML Engineer at Symphony, you’ll lead AI initiatives for global enterprise clients—shaping solutions, building PoCs, and architecting production-ready ML systems across domains from creative audio/video to complex business workflows. This is a hands-on technical leadership role where you define strategy, design end-to-end architectures, build core components, and set the technical standards for each engagement.

What would be your responsibilities if you join us?

Translate business challenges into AI/ML solutions; lead discovery workshops.

Build and present PoCs to validate feasibility and value.

Contribute technical input to proposals, scoping, and estimations.

Develop models across NLP, CV, audio, and generative AI.

Lead fine-tuning and PEFT methods (LoRA, QLoRA, instruction tuning).

Assess architectural trade-offs: fine-tuning vs. prompting vs. off-the-shelf.

Architect scalable deployments (APIs, batch, real-time).

Implement CI/CD for ML: automated training, evaluation, versioning, deployment.

Define monitoring, drift detection, and optimization standards.

Collaborate with data engineers on pipelines and feature engineering.

Stay current with foundation models and multimodal innovations.

Set coding standards and lead design/architecture reviews.

Mentor engineers and guide research-to-production workflows.

Communicate technical decisions to both engineering and executive audiences.

Kvalifikacije

There are some requirements that will make you stand out:

Bachelor's degree in Computer Science, Information Technology, or a related field, or equivalent practical experience.

Minimum 10+ years in software engineering or ML, with 5+ years focused on production ML systems

Strong background in consulting, professional services, or client-facing technical leadership.

Track record of delivering ML solutions across multiple domains or industries.

Expert Python; strong software engineering fundamentals.

Deep experience with PyTorch or TensorFlow and modern architectures (Transformers, Diffusion models).

Hands-on with LLMs: fine-tuning open-source models (Llama, Mistral), parameter-efficient methods, and evaluation strategies.

Model serving, containerisation (Docker/Kubernetes), and CI/CD pipelines.

Proficiency with AWS SageMaker, GCP Vertex AI, or Azure ML.