This role is for one of our clients
Industry: Software Development
Seniority level: Mid-Senior level
Min Experience: 4 years
Location: Remote (India)
JobType: full-time
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. 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.
This role requires strong collaboration with engineering, product, and business teams to design robust AI architectures that align with organizational goals while ensuring scalability, performance, and responsible AI practices.
Required Qualifications
- 4+ years of experience in software engineering or architecture roles with strong exposure to AI/ML systems.
- Strong knowledge of modern neural network architectures such as Transformers, CNNs, and RNNs.
- Experience designing scalable and distributed architectures for AI-powered applications.
- Hands-on experience with cloud platforms such as AWS, Azure, or Google Cloud.
- Experience with containerization and orchestration technologies including Docker and Kubernetes.
- Strong understanding of microservices architecture, RESTful APIs, and distributed system design.
- Experience working with MLOps / LLMOps pipelines including model training, deployment, monitoring, and lifecycle management.
- Familiarity with large-scale data systems and modern database technologies.
- Experience translating business requirements into scalable AI solution architectures.
- Strong documentation skills for architecture designs, workflows, and technical decision-making.
- Comfortable working in a startup or fast-paced environment with strong ownership and leadership mindset.
Key Responsibilities
Design and oversee the development of
scalable generative AI systems and enterprise-grade AI platforms. Establish robust architectures that support
model training, inference, monitoring, and lifecycle management in production environments. Direct the selection, customization, and enhancement of
state-of-the-art generative AI and large language models.
Develop and execute
APIs, microservices, and integration frameworks to incorporate AI capabilities into enterprise applications. Ensure that AI platforms meet stringent standards for
performance, reliability, security, and scalability, while also adhering to data governance and privacy regulations.
Collaborate closely with product, engineering, and business teams to outline
technical requirements and approaches to AI architecture. Architect
end-to-end pipelines for deploying and monitoring AI models, ensuring seamless integration with existing systems.
Guide architectural decisions for
LLM applications, AI workflows, and distributed AI infrastructure. Institute best practices for ethical AI development, including strategies to mitigate risks like
model hallucinations, bias, and reliability challenges.
Provide technical mentorship and guidance to engineering teams, while contributing to the formulation of
long-term technology strategies and the advancement of AI platforms.
Preferred Qualifications
- Experience with Generative AI frameworks and orchestration tools such as LangChain, LangGraph, or similar platforms.
- Expertise in prompt engineering, LLM fine-tuning techniques (LoRA, RLHF, PEFT), and methods for model optimization.
- Familiarity with performance optimization techniques for AI workloads, including GPU/TPU acceleration, quantization, pruning, and model distillation.
- Experience with AI observability and monitoring solutions for evaluating model performance, drift, and anomalies.
- Understanding of AI governance, security, and compliance frameworks such as GDPR or SOC 2.
Prior experience in developing
enterprise-scale AI or LLM-based products.
Skills
MLOps / LLMOps pipelines
AWS, Azure, or Google Cloud
RESTful APIs
Docker and Kubernetes
