Overview:
ROLE :
The Technical Architect is the engineering owner of the product. They receive business requirements from the Product
Architect and translate them into coherent technical strategies - choosing the right technology stack, defining system
design, and leading the engineering team to build products that are scalable, secure, and future-ready. Within Prodapt's
AI-native portfolio, this role is the custodian of platform integrity and the technical compass for all engineering decisions.
Responsibilities:
CORE RESPONSIBILITIES
- Own end-to-end technical architecture across core, AI/ML platform, data infrastructure, APIs, and integration layers.
- Translate product requirements (PRDs) into detailed technical designs, system diagrams, & engineering specifications.
- Make authoritative technology choices: frameworks, cloud providers, data stores, AI/LLM orchestration tooling with
clear rationale.
- Define and enforce engineering standards: coding guidelines, security posture, performance SLOs, and CI/CD
practices.
- Lead, mentor, and grow a team of Forward Deployment Engineers - conduct architecture and code reviews.
- Identify and mitigate technical risks early; build resilience and observability into every product layer.
- Collaborate with Product Architect on feasibility, trade-off analysis, and build-vs-buy decisions.
- Drive prototype-to-production engineering excellence; own technical debt strategy and platform evolution roadmap.
MUST-HAVE SKILLS & EXPERIENCE
- Strong product engineering experience in one of the platforms: ServiceNow, Salesforce, Netcracker, Amdocs, Ericson.
- Expert-level software engineering in Python, Go, or Java with strong polyglot instincts across the stack.
- Deep experience designing distributed systems, microservices, and event-driven architectures at scale.
- Hands-on AI/ML platform engineering: LLM integration, vector databases, RAG pipelines, model serving (vLLM,
Ollama, TensorRT-LLM, Triton, etc.).
- Cloud-native expertise across AWS / Azure / GCP: Kubernetes, Terraform, observability stacks, and FinOps.
- Strong data engineering background: streaming (Kafka/Flink), warehousing (Snowflake/BigQuery), and lineage tooling.
- Security-first mindset: zero-trust networking, secrets management, vulnerability remediation at the infra layer.
MINDSET & BEHAVIOURS
- Pragmatic visionary - balances architectural elegance with delivery speed and operational reality.
Requirements:
MUST-HAVE SKILLS & EXPERIENCE
- Strong product engineering experience in one of the platforms: ServiceNow, Salesforce, Netcracker, Amdocs, Ericson.
- Expert-level software engineering in Python, Go, or Java with strong polyglot instincts across the stack.
- Deep experience designing distributed systems, microservices, and event-driven architectures at scale.
- Hands-on AI/ML platform engineering: LLM integration, vector databases, RAG pipelines, model serving (vLLM,
Ollama, TensorRT-LLM, Triton, etc.).
- Cloud-native expertise across AWS / Azure / GCP: Kubernetes, Terraform, observability stacks, and FinOps.
- Strong data engineering background: streaming (Kafka/Flink), warehousing (Snowflake/BigQuery), and lineage tooling.
- Security-first mindset: zero-trust networking, secrets management, vulnerability remediation at the infra layer.
MINDSET & BEHAVIOURS
- Pragmatic visionary - balances architectural elegance with delivery speed and operational reality.
- Trusted technical authority - engineers follow their lead; can say no with reasoned alternatives.
- Systems thinker - sees second-order effects of technology choices on cost, scalability, and team velocity.
- Continuous learner - actively evaluates emerging AI/ML tooling and brings proven innovations to the team.
PREFERRED QUALIFICATIONS
- B.S. / M.S. in Computer Science, Systems Engineering, or related field.
- Published architecture patterns, open-source contributions, or conference talks in AI/infra domain.
- Experience as a technical lead in a telecom, network automation, or enterprise SaaS environment.