Mar 2025 – Present · 1 yr 2 mos · Remote · Remote

• Concerto is a powerful modeling language used in the Accord Project for defining domain-specific schemas and contracts. However, ensuring consistent style, enforcing best practices, and preventing anti-patterns in Concerto models currently requires manual review. A linter would automate these checks, improving code quality, reducing errors, and enforcing organizational standards. Solution Approach We propose building a TypeScript-based linter for Concerto that: Parses .cto files into an AST using @accordproject/concerto-core. Applies configurable rules to validate naming conventions, required decorators, forbidden constructs, and structural constraints. Provides a CLI & IDE integration for seamless adoption in development workflows. Supports custom rule definitions (e.g., enforce @Term on all properties, restrict regex forward references). Key Deliverables ✅ Rule Engine – Extensible system for defining and running lint rules (e.g., camelCase checks, mandatory validators). ✅ Predefined Rules – Covering Accord Project’s best practices (e.g., namespace conventions, no raw String types). ✅ CLI Tool – Command-line interface with configurable severity levels (error, warning, off). ✅ Configuration Support – Per-project rule customization via .concerto-lint.json. ✅ Testing & Docs – Unit tests + user guide for rule creation and integration. Why This Matters Enforces Consistency: Ensures all models follow naming and structural conventions. Prevents Errors: Catches invalid patterns (e.g., missing @Term, unsafe regex) early. Extensible: Teams can add custom rules for domain-specific needs.

Jun 2024 – Present · 1 yr 11 mos

Sep 2023 – Jun 2024 · 9 mos

• On their AI team, CIOs and technology innovation leaders need to have data scientists, data engineers and complement the team with AI architects and machine learning (ML) engineers. Together they can envision, build, deploy and operationalize an end-to-end ML/AI pipeline

Jun 2024 – Present · 1 yr 11 mos · From home · Remote

Jun 2024 – Aug 2025 · 1 yr 2 mos · From home · Remote

Oct 2023 – Present · 2 yrs 7 mos · Uttar Pradesh, India · Hybrid

• Designing machine learning systems and self-running artificial intelligence (AI) software to automate predictive models.

Oct 2023 – Present · 2 yrs 7 mos · Uttar Pradesh, India · Remote

Aug 2023 – Present · 2 yrs 9 mos · From home · On-site

• TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications.
• TensorFlow was originally developed by researchers and engineers working within the Machine Intelligence team at Google Brain to conduct research in machine learning and neural networks. However, the framework is versatile enough to be used in other areas as well.

Sep 2022 – Sep 2023 · 1 yr · Ghaziabad, Uttar Pradesh, India